For a complete list, see Google
Scholar. My students' names appear with brown font.
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GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
[PAPER][PAGE WITH CODE & DATA]
Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan, Vladimir G. Kim, Matheus Gadelha, Melinos Averkiou, Siddhartha Chaudhuri, Evangelos Kalogerakis
Proceedings of ACM SIGGRAPH 2024
Abstract: We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes. The key ingredient of our method is a neural skeleton-based representation encoding information on both shape topology and geometry. Through a denoising diffusion probabilistic model, our method first generates skeleton-based representations following the Medial Axis Transform (MAT), then generates surfaces through a skeleton-driven neural implicit formulation. The neural implicit takes into account the topological and geometric information stored in the generated skeleton representations to yield surfaces that are more topologically and geometrically accurate compared to previous neural field formulations. We discuss applications of our method in shape synthesis and point cloud reconstruction tasks, and evaluate our method both qualitatively and quantitatively. We demonstrate significantly more faithful surface reconstruction and diverse shape generation results compared to the state-of-the-art, also involving challenging scenarios of reconstructing and synthesizing structurally complex, high-genus shape surfaces from Thingi10K and ShapeNet.
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VecFusion: Vector Font Generation with Diffusion
[PAPER][PAGE WITH CODE & DATA]
Vikas Thamizharasan*, Difan Liu*, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis
(* indicates equal contribution)
Proceedings of the Computer Vision and Pattern Recognition (CVPR) 2024 (Selected as highlight)
Abstract: We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution, rasterized fonts with auxiliary control point information, capturing the global style and shape of the font, while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves, our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that, in contrast to previous generative models for vector graphics, our new cascaded vector diffusion model generates higher quality vector fonts, with complex structures and diverse styles.
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NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation
[PAPER][PAGE WITH CODE & DATA]
Vikas Thamizharasan, Difan Liu, Matthew Fisher, Nanxuan Zhao, Evangelos Kalogerakis, Michal Lukáč
Proceedings of the Computer Vision and Pattern Recognition (CVPR) 2024
Abstract: The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations, such as vector graphics. Unfortunately due to their variable structure and scarcity of vector training data, directly applying diffusion models on this domain remains a challenging problem. Using workarounds like optimization via Score Distillation Sampling (SDS) is also fraught with difficulty, as vector representations are non-trivial to directly optimize and tend to result in implausible geometries such as redundant or self-intersecting shapes. NIVeL addresses these challenges by reinterpreting the problem on an alternative, intermediate domain which preserves the desirable properties of vector graphics -- mainly sparsity of representation and resolution-independence. This alternative domain is based on neural implicit fields expressed in a set of decomposable, editable layers. by construction and allow for changes in topology while capturing the visual features of the modelled output. Based on our experiments, NIVeL produces text-to-vector graphics results of significantly better quality than the state-of-the-art.
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Open3DIS: Open-vocabulary 3D Instance Segmentation with 2D Mask Guidance
[PAPER] [PAGE WITH CODE & DATA]
Phuc Nguyen*, Tuan Duc Ngo*, Evangelos Kalogerakis, Chuang Gan, Anh Tran, Cuong Pham, Khoi Nguyen
(* indicates equal contribution)
Proceedings of the Computer Vision and Pattern Recognition (CVPR) 2024
Abstract: We introduce Open3DIS, a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes, scales, and colors, making precise instance-level identification a challenging task. Recent advancements in Open-Vocabulary scene understanding have made significant strides in this area by employing class-agnostic 3D instance proposal networks for object localization and learning queryable features for each 3D mask. While these methods produce high-quality instance proposals, they struggle with identifying small-scale and geometrically ambiguous objects. The key idea of our method is a new module that aggregates 2D instance masks across frames and maps them to geometrically coherent point cloud regions as high-quality object proposals addressing the above limitations. These are then combined with 3D class-agnostic instance proposals to include a wide range of objects in the real world. To validate our approach, we conducted experiments on three prominent datasets, including ScanNet200, S3DIS, and Replica, demonstrating significant performance gains in segmenting objects with diverse categories over the state-of-the-art approaches.
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ANISE: Assembly-based Neural Implicit Surface rEconstruction
[PAPER] [PAGE WITH CODE & DATA]
Dmitry Petrov, Matheus Gadelha, Radomir Mech, Evangelos Kalogerakis
IEEE Transactions on Visualization and Computer Graphics, 2023
(also presented at SGP 2023)
Abstract:
We present ANISE, a method that reconstructs a 3D shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape is formulated as an assembly of neural implicit functions, each representing a different part instance. In contrast to previous approaches, the prediction of this representation proceeds in a coarse-to-fine manner. Our model first reconstructs a structural arrangement of the shape in the form of geometric transformations of its part instances. Conditioned on them, the model predicts part latent codes encoding their surface geometry. Reconstructions can be obtained in two ways: (i) by directly decoding the part latent codes to part implicit functions, then combining them into the final shape; or (ii) by using part latents to retrieve similar part instances in a part database and assembling them in a single shape. We demonstrate that, when performing reconstruction by decoding part representations into implicit functions, our method achieves state-of-the-art part-aware reconstruction results from both images and sparse point clouds. When reconstructing shapes by assembling parts retrieved from a dataset, our approach significantly outperforms traditional shape retrieval methods even when significantly restricting the database size. We present our results in well-known sparse point cloud reconstruction and single-view reconstruction benchmarks.
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Cross-Shape Attention for Part Segmentation of 3D Point Clouds
[PAPER] [PAGE WITH CODE & DATA]
Marios Loizou*, Siddhant Garg*, Dmitry Petrov*, Melinos Averkiou, Evangelos Kalogerakis
(* indicates equal contribution)
Computer Graphics Forum, vol. 42, no. 5
(also in the Proceedings of SGP 2023)
Abstract:
We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable interactions between a shape's point-wise features and those of other shapes. The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation. Our method also proposes a shape retrieval measure to select suitable shapes for cross-shape attention operations for each test shape. Our experiments demonstrate that our approach yields state-of-the-art results in the popular PartNet dataset.
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Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging
[PAPER]
Ke Xiao, Erik Learned-Miller, Evangelos Kalogerakis, James Priest, Madalina Fiterau
Proceedings of Medical Image Computing and Computer-Assisted Intervention - MICCAI 2023
Abstract:
Mitral regurgitation (MR) is a heart valve disease with potentially
fatal consequences that can only be forestalled through timely
diagnosis and treatment. Traditional diagnosis methods are expensive,
labor-intensive and require clinical expertise, posing a barrier to screening
for MR. To overcome this impediment, we propose a new semisupervised
model for MR classification called CUSSP. CUSSP operates
on cardiac magnetic resonance (CMR) imaging slices of the 4-chamber
view of the heart. It uses standard computer vision techniques and contrastive
models to learn from large amounts of unlabeled data, in conjunction
with specialized classifiers to establish the first ever automated
MR classification system using CMR imaging sequences. Evaluated on a
test set of 179 labeled sequences, CUSSP
attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the
first benchmark result for detecting MR from CMR imaging sequences.
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MoRig: Motion-Aware Rigging of Character Meshes from Point Clouds
[PAPER] [VIDEO] [PAGE WITH CODE & DATA]
Zhan Xu, Yang Zhou, Li Yi, Evangelos Kalogerakis
Proceedings of ACM SIGGRAPH ASIA 2022
Abstract:
We present MoRig, a method that automatically rigs character meshes driven by single-view point cloud streams capturing the motion of performing characters. Our method is also able to animate the 3D meshes according to the captured point cloud motion. At the heart of our approach lies a deep neural network that encodes motion cues from the point clouds into features that are informative about the articulated parts of the performing character. These features guide the inference of an appropriate skeletal rig for the input mesh, which is then animated based on the input point cloud motion. Our method can rig and animate diverse characters, including humanoids, quadrupeds, and toys with varying articulations. It is designed to account for occluded regions in the input point cloud sequences and any mismatches in the part proportions between the input mesh and captured character. Compared to other rigging approaches that ignore motion cues, our method produces more accurate skeletal rigs, which are also more appropriate for re-targeting motion from captured characters.
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ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
[PAPER]
[PAGE WITH CODE & DATA]
Difan Liu, Sandesh Shetty, Tobias Hinz, Matthew Fisher, Richard Zhang, Taesung Park, Evangelos Kalogerakis
ACM Transactions on Graphics, Vol. 41, No. 4, 2022
(also in the Proceedings of ACM SIGGRAPH 2022)
Abstract:
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map. Our architecture is based on a transformer with a novel attention mechanism. Our key idea is to sparsify the transformer's attention matrix at high resolutions, guided by dense attention extracted at lower image resolution. While previous attention mechanisms are computationally too expensive for handling high-resolution images or are overly constrained within specific image regions hampering long-range interactions, our proposed attention mechanism is both computationally efficient and effective. Our sparsified attention mechanism is able to capture long-range interactions and context, leading to synthesizing interesting phenomena in scenes, such as reflections of landscapes onto water or flora consistent with the rest of the landscape, that were not possible to generate reliably with previous convnets and transformer approaches. We present qualitative and quantitative results, along with user studies, demonstrating the effectiveness of our method.
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MvDeCor: Multi-view Dense Correspondence Learning for Fine-Grained 3D Segmentation
[PAPER]
[PAGE WITH CODE & DATA]
Gopal Sharma, Kangxue Yin, Subhransu Maji, Evangelos Kalogerakis, Or Litany, Sanja Fidler
Proceedings of the European Conference on Computer Vision (ECCV) 2022
Abstract:
We propose to utilize self-supervised techniques in the 2D
domain for fine-grained 3D shape segmentation tasks. This is inspired by
the observation that view-based surface representations are more effective
at modeling high-resolution surface details and texture than their 3D
counterparts based on point clouds or voxel occupancy. Specifically, given
a 3D shape, we render it from multiple views, and set up a dense correspondence
learning task within the contrastive learning framework. As a
result, the learned 2D representations are view-invariant and geometrically
consistent, leading to better generalization when trained on a limited
number of labeled shapes than alternatives based on self-supervision
in 2D or 3D alone. Experiments on textured (RenderPeople) and untextured
(PartNet) 3D datasets show that our method outperforms state-ofthe-
art alternatives in fine-grained part segmentation. The improvements
over baselines are greater when only a sparse set of views is available for
training or when shapes are textured, indicating that MvDeCor benefits
from both 2D processing and 3D geometric reasoning.
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Audio-driven Neural Gesture Reenactment with Video Motion Graphs
[PAPER]
[PAGE WITH CODE & DATA]
Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, Evangelos Kalogerakis
Proceedings of the Computer Vision and Pattern Recognition (CVPR) 2022
Abstract:
Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, we propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, we developed an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. Our system generates reenactments that are consistent with both the audio rhythms and the speech content. We evaluate our synthesized video quality quantitatively, qualitatively, and with user studies, demonstrating that our method produces videos of much higher quality and consistency with the target audio compared to previous work and baselines.
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APES: Articulated Part Extraction from Sprite Sheets
[PAPER]
[PAGE WITH CODE & DATA]
Zhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat, Li Yi, Evangelos Kalogerakis
Proceedings of the Computer Vision and Pattern Recognition (CVPR) 2022
Abstract:
Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated body parts, e.g. head, torso and limbs, that can be re-assembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively.
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PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
[PAPER]
[PAGE WITH CODE & DATA]
Gopal Sharma, Bidya Dash, Aruni RoyChowdhury, Matheus Gadelha, Marios Loizou, Liangliang Cao, Rui Wang, Erik Learned-Miller, Subhransu Maji, Evangelos Kalogerakis
Computer Graphics Forum, Vol. 41, No. 5, 2022
(also in the Proceedings of SGP 2022)
Abstract:
We present PRIFIT, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks. PRIFIT
combines geometric primitive fitting with point-based representation learning. Its key idea is to learn point representations
whose clustering reveals shape regions that can be approximated well by basic geometric primitives, such as cuboids and ellipsoids.
The learned point representations can then be re-used in existing network architectures for 3D point cloud segmentation,
and improves their performance in the few-shot setting. According to our experiments on the widely used ShapeNet and PartNet
benchmarks, PRIFIT outperforms several state-of-the-art methods in this setting, suggesting that decomposability into primitives
is a useful prior for learning representations predictive of semantic parts. We present a number of ablative experiments
varying the choice of geometric primitives and downstream tasks to demonstrate the effectiveness of the method.
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BuildingNet: Learning to
Label 3D Buildings
[PAPER][VIDEO][PAGE WITH CODE & DATA]
Pratheba Selvaraju, Mohamed Nabail, Marios Loizou, Maria Maslioukova, Melinos Averkiou, Andreas
Andreou, Siddhartha Chaudhuri, Evangelos Kalogerakis
Proceedings of the International Conference on
Computer Vision (ICCV) 2021 (Selected for Oral
Presentation)
Abstract: We introduce BuildingNet:
(a) a large-scale dataset of 3D building models whose
exteriors are consistently labeled, (b) a graph neural
network that labels building meshes by analyzing spatial
and structural relations of their geometric primitives. To
create our dataset, we used crowdsourcing combined with
expert guidance, resulting in 513K annotated mesh
primitives, grouped into 292K semantic part components
across 2K building models. The dataset covers several
building categories, such as houses, churches,
skyscrapers, town halls, libraries, and castles. We
include a benchmark for evaluating mesh and point cloud
labeling. Buildings have more challenging structural
complexity compared to objects in existing benchmarks
(e.g., ShapeNet, PartNet), thus, we hope that our dataset
can nurture the development of algorithms that are able to
cope with such large-scale geometric data for both vision
and graphics tasks e.g., 3D semantic segmentation,
part-based generative models, correspondences, texturing,
and analysis of point cloud data acquired from real-world
buildings. Finally, we show that our mesh-based graph
neural network significantly improves performance over
several baselines for labeling 3D meshes.
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Neural Strokes: Stylized
Line Drawing of 3D Shapes
[PAPER] [PAGE WITH CODE & DATA]
Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis
Proceedings of the International Conference on
Computer Vision (ICCV) 2021
Abstract: This paper introduces a
model for producing stylized line drawings from 3D shapes.
The model takes a 3D shape and a viewpoint as input, and
outputs a drawing with textured strokes, with variations
in stroke thickness, deformation, and color learned from
an artist's style. The model is fully differentiable. We
train its parameters from a single training drawing of
another 3D shape. We show that, in contrast to previous
image-based methods, the use of a geometric representation
of 3D shape and 2D strokes allows the model to transfer
important aspects of shape and texture style while
preserving contours. Our method outputs the resulting
drawing in a vector representation, enabling richer
downstream analysis or editing in interactive
applications.
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Projective Urban Texturing
[PAPER]
Yiangos Georgiou, Melinos Averkiou, Tom Kelly, Evangelos Kalogerakis
Proceedings of the International Conference on 3D Vision (3DV) 2021
Abstract:
This paper proposes a method for automatic generation of textures for 3D city meshes in immersive urban environments. Many recent pipelines capture or synthesize large quantities of city geometry using scanners or procedural modeling pipelines. Such geometry is intricate and realistic, however the generation of photo-realistic textures for such large scenes remains a problem. We propose to generate textures for input target 3D meshes driven by the textural style present in readily available datasets of panoramic photos capturing urban environments. Re-targeting such 2D datasets to 3D geometry is challenging because
the underlying shape, size, and layout of the urban structures in the photos do not correspond to the ones in the target meshes. Photos also often have objects (e.g., trees, vehicles) that may not even be present in the target geometry. To address these issues we present a method, called Projective Urban Texturing (PUT), which re-targets textural style from real-world panoramic images to unseen urban meshes. PUT relies on contrastive and adversarial training of a neural architecture designed for unpaired image-to-texture translation. The generated textures are stored in a texture atlas applied to the target 3D mesh geometry.
We demonstrate both quantitative and qualitative evaluation of the generated textures.
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MakeItTalk: Speaker-Aware Talking Head Animation
[PAPER][VIDEO][PAGE
WITH CODE & DATA]
Yang Zhou, Xintong Han, Eli Shechtman, Jose
Echevarria, Evangelos Kalogerakis, Dingzeyu Li
ACM Transactions on Graphics, Vol. 39, No. 6,
2020 (to appear)
(also in the Proceedings of ACM SIGGRAPH ASIA
2020)
Abstract: We present a method that
generates expressive talking-head videos from a single
facial image with audio as the only input. In contrast to
previous attempts to learn direct mappings from audio to
raw pixels for creating talking faces, our method first
disentangles the content and speaker information in the
input audio signal. The audio content robustly controls
the motion of lips and nearby facial regions, while the
speaker information determines the specifics of facial
expressions and the rest of the talking-head dynamics.
Another key component of our method is the prediction of
facial landmarks reflecting the speaker-aware dynamics.
Based on this intermediate representation, our method
works with many portrait images in a single unified
framework, including artistic paintings, sketches, 2D
cartoon characters, Japanese mangas, and stylized
caricatures. In addition, our method generalizes well for
faces and characters that were not observed during
training. We present extensive quantitative and
qualitative evaluation of our method, in addition to user
studies, demonstrating generated talking-heads of
significantly higher quality compared to prior
state-of-the-art methods.
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ParSeNet: A Parametric Surface Fitting Network
for 3D Point Clouds
[PAPER]
[PAGE
WITH CODE & DATA]
Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomir
Mech
Proceedings of the European Conference on
Computer Vision (ECCV) 2020
Abstract: We propose a novel,
end-to-end trainable, deep network called ParSeNet that
decomposes a 3D point cloud into parametric surface
patches, including B-spline patches as well as basic
geometric primitives. ParSeNet is trained on a large-scale
dataset of man-made 3D shapes and captures high-level
semantic priors for shape decomposition. It handles a much
richer class of primitives than prior work, and allows us
to represent surfaces with higher fidelity. It also
produces repeatable and robust parametrizations of a
surface compared to purely geometric approaches. We
present extensive experiments to validate our approach
against analytical and learning-based alternatives.
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Label-Efficient Learning on Point Clouds using
Approximate Convex Decompositions
[PAPER]
[PAGE WITH
CODE & DATA]
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma, Evangelos Kalogerakis, Liangliang Cao, Erik
Learned-Miller, Rui Wang, Subhransu Maji
Proceedings of the European Conference on Computer
Vision (ECCV) 2020
Abstract: The problems of shape
classification and part segmentation from 3D point clouds
have garnered increasing attention in the last few years.
Both of these problems, however, suffer from relatively
small training sets, creating the need for statistically
efficient methods to learn 3D shape representations. In
this paper, we investigate the use of Approximate Convex
Decompositions (ACD) as a self-supervisory signal for
label-efficient learning of point cloud representations.
We show that using ACD to approximate ground truth
segmentation provides excellent self-supervision for
learning 3D point cloud representations that are highly
effective on downstream tasks. We report improvements over
the state-of-the-art for unsupervised representation
learning on the ModelNet40 shape classification dataset
and significant gains in few-shot part segmentation on the
ShapeNetPart dataset.
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RigNet: Neural Rigging for Articulated
Characters
[PAPER][VIDEO]
[PAGE
WITH CODE & DATA]
Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Chris Landreth, Karan Singh
ACM Transactions on Graphics,
Vol. 39, No. 4, 2020
(also in the Proceedings of ACM
SIGGRAPH 2020)
Abstract: We present RigNet, an
end-to-end automated method for producing animation rigs
from input character models. Given an input 3D model
representing an articulated character, RigNet predicts a
skeleton that matches the animator expectations in joint
placement and topology. It also estimates surface skin
weights based on the predicted skeleton. Our method is
based on a deep architecture that directly operates on the
mesh representation without making assumptions on shape
class and structure. The architecture is trained on a
large and diverse collection of rigged models, including
their mesh, skeletons and corresponding skin weights. Our
evaluation is three-fold: we show better results than
prior art when quantitatively compared to animator rigs;
qualitatively we show that our rigs can be expressively
posed and animated at multiple levels of detail; and
finally, we evaluate the impact of various algorithm
choices on our output rigs.
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Learning Part Boundaries from 3D Point Clouds
[PAPER]
[PAGE
WITH CODE & DATA]
Marios Loizou, Melinos Averkiou, Evangelos Kalogerakis
Computer Graphics Forum,
Vol. 39, No. 5, 2020
(also in the Proceedings of SGP 2020)
Abstract: We present a method that
detects boundaries of parts in 3D shapes represented as
point clouds. Our method is based on a graph convolutional
network architecture that outputs a probability for a
point to lie in an area that separates two or more parts
in a 3D shape. Our boundary detector is quite generic: it
can be trained to localize boundaries of semantic parts or
geometric primitives commonly used in 3D\ modeling. Our
experiments demonstrate that our method can extract more
accurate boundaries that are closer to ground-truth ones
compared to alternatives. We also demonstrate an
application of our network to fine-grained semantic shape
segmentation, where we also show improvements in terms of
part labeling performance.
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Neural Contours: Learning to Draw Lines from
3D Shapes
[PAPER]
[PAGE
WITH CODE & DATA]
Difan Liu, Mohamed Nabail, Evangelos Kalogerakis, Aaron Hertzmann
Proceedings of the Computer Vision and Pattern
Recognition (CVPR) 2020
Abstract: This paper introduces a
method for learning to generate line drawings from 3D
models. Our architecture incorporates a differentiable
module operating on geometric features of the 3D model,
and an image-based module operating on view-based shape
representations. At test time, geometric and view-based
reasoning are combined by a neural ranking module to
create a line drawing. The model is trained on a large
number of crowdsourced comparisons of line drawings.
Experiments demonstrate that our method achieves
significant improvements in line drawing over the
state-of-the-art when evaluated on standard benchmarks,
resulting in drawings that are comparable to those
produced by experienced human artists.
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SceneGraphNet: Neural Message Passing for 3D
Indoor Scene Augmentation
[PAPER]
[PAGE
WITH CODE & DATA]
Yang Zhou, Zachary While, Evangelos Kalogerakis
Proceedings of the International Conference on
Computer Vision (ICCV) 2019
Abstract: In this paper we propose a
neural message passing approach to augment an input 3D
indoor scene with new objects matching their surroundings.
Given an input, potentially incomplete, 3D scene and a
query location, our method predicts a probability
distribution over object types that fit well in that
location. Our distribution is predicted though passing
learned messages in a dense graph whose nodes represent
objects in the input scene and edges represent spatial and
structural relationships. By weighting messages through an
attention mechanism, our method learns to focus on the
most relevant surrounding scene context to predict new
scene objects. We found that our method significantly
outperforms state-of-the-art approaches in terms of
correctly predicting objects missing in a scene based on
our experiments in the SUNCG dataset. We also demonstrate
other applications of our method, including context-based
3D object recognition and iterative scene generation.
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Predicting Animation Skeletons for 3D
Articulated Models via Volumetric Nets
[PAPER]
[PAGE
WITH CODE & DATA]
Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Karan Singh
Proceedings of the
International Conference on 3D Vision (3DV) 2019 (Selected for Oral
Presentation)
Abstract: We present a learning
method for predicting animation skeletons for input 3D
models of articulated characters. In contrast to previous
approaches that fit pre-defined skeleton templates or
predict fixed sets of joints, our method produces an
animation skeleton tailored for the structure and geometry
of the input 3D model. Our architecture is based on a
stack of hourglass modules trained on a large dataset of
3D rigged characters mined from the web. It operates on
the volumetric representation of the input 3D shapes
augmented with geometric shape features that provide
additional cues for joint and bone locations. Our method
also enables intuitive user control of the level-of-detail
for the output skeleton. Our evaluation demonstrates that
our approach predicts animation skeletons that are much
more similar to the ones created by humans compared to
several alternatives and baselines.
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Learning Point Embeddings from Shape
Repositories for Few-Shot Segmentation
[PAPER]
Gopal Sharma, Evangelos Kalogerakis,
Subhransu Maji
Proceedings of the International Conference on
3D Vision (3DV) 2019 (Selected for Oral
Presentation)
Abstract: User generated 3D shapes
in online repositories contain rich information about
surfaces, primitives, and their geometric relations, often
arranged in a hierarchy. We present a framework for
learning representations of 3D shapes that reflect the
information present in this meta data and show that it
leads to improved generalization for semantic segmentation
tasks. Our approach is a point embedding network that
generates a vectorial representation of the 3D point such
that it reflects the grouping hierarchy and tag data. The
main challenge is that the data is highly variable and
noisy. To this end, we present tree-aware metrics to
supervise a metric-learning approach and demonstrate that
such learned embeddings offer excellent transfer to
semantic segmentation tasks, especially when training data
is limited.
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Deep Part Induction from Articulated Object
Pairs
[PAPER]
[PAGE
WITH CODE & DATA]
Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas
ACM Transactions on Graphics, Vol. 37, No. 6,
2018
(also in the Proceedings of ACM SIGGRAPH ASIA
2018)
Abstract: Object functionality is
often expressed through part articulation -- as when the
two rigid parts of a scissor pivot against each other to
perform the cutting function. Such articulations are often
similar across objects within the same functional
category. In this paper, we explore how the observation of
different articulation states provides evidence for part
structure and motion of 3D objects. Our method takes as
input a pair of unsegmented shapes representing two
different articulation states of two functionally related
objects, and induces their common parts along with their
underlying rigid motion. This is a challenging setting, as
we assume no prior shape structure, no prior shape
category information, no consistent shape orientation, the
articulation states may belong to objects of different
geometry, plus we allow inputs to be noisy and partial
scans, or point clouds lifted from RGB images. Our method
learns a neural network architecture with three modules
that respectively propose correspondences, estimate 3D
deformation flows, and perform segmentation. To achieve
optimal performance, our architecture alternates between
correspondence, deformation flow, and segmentation
prediction iteratively in an ICP-like fashion. Our results
demonstrate that our method significantly outperforms
state-of-the-art techniques in the task of discovering
articulated parts of objects. In addition, our part
induction is object-class agnostic and successfully
generalizes to new and unseen objects.
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VisemeNet: Audio-Driven Animator-Centric
Speech Animation
[PAPER][VIDEO][PAGE
WITH CODE & DATA]
Yang Zhou, Zhan Xu, Chris Landreth, Evangelos Kalogerakis, Subhransu Maji, Karan Singh
ACM Transactions on Graphics, Vol. 37, No. 4,
2018
(also in the Proceedings of ACM SIGGRAPH 2018)
Abstract: We present a novel
deep-learning based approach to producing animator-centric
speech motion curves that drive a JALI or standard
FACS-based production face-rig, directly from input audio.
Our three-stage Long Short-Term Memory (LSTM) network
architecture is motivated by psycho-linguistic insights:
segmenting speech audio into a stream of phonetic-groups
is sufficient for viseme construction; speech styles like
mumbling or shouting are ly co-related to the motion
of facial landmarks; and animator style is encoded in
viseme motion curve profiles. Our contribution is an
automatic real-time lip-synchronization from audio
solution that integrates seamlessly into existing
animation pipelines. We evaluate our results by:
cross-validation to ground-truth data; animator critique
and edits; visual comparison to recent deep-learning
lip-synchronization solutions; and showing our approach to
be resilient to diversity in speaker and language.
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Learning Local Shape Descriptors from Part
Correspondences With Multi-view Convolutional Networks
[PAPER][PAGE WITH CODE
& DATA]
Haibin Huang, Evangelos Kalogerakis,
Siddhartha Chaudhuri, Duygu Ceylan, Vladimir Kim,
Ersin Yumer
ACM Transactions on Graphics, Vol. 37, No. 1,
2018
(also in SIGGRAPH 2018)
Abstract: We present a new local
descriptor for 3D shapes, directly applicable to a wide
range of shape analysis problems such as point
correspondences, semantic segmentation, affordance
prediction, and shape-to-scan matching. The descriptor is
produced by a convolutional network that is trained to
embed geometrically and semantically similar points close
to one another in descriptor space. The network processes
surface neighborhoods around points on a shape that are
captured at multiple scales by a succession of
progressively zoomed out views, taken from carefully
selected camera positions. We leverage two extremely large
sources of data to train our network. First, since our
network processes rendered views in the form of 2D images,
we repurpose architectures pre-trained on massive image
datasets. Second, we automatically generate a synthetic
dense point correspondence dataset by non-rigid alignment
of corresponding shape parts in a large collection of
segmented 3D models. As a result of these design choices,
our network effectively encodes multi-scale local context
and fine-grained surface detail. Our network can be
trained to produce either category-specific descriptors or
more generic descriptors by learning from multiple shape
categories. Once trained, at test time, the network
extracts local descriptors for shapes without requiring
any part segmentation as input. Our method can produce
effective local descriptors even for shapes whose category
is unknown or different from the ones used while training.
We demonstrate through several experiments that our
learned local descriptors are more discriminative compared
to state of the art alternatives, and are effective in a
variety of shape analysis applications.
|
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SPLATNet: Sparse Lattice Networks for Point
Cloud Processing
[PAPER][PAGE WITH
CODE & DATA]
Hang Su, Varun Jampani, Deqing Sun,
Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan
Yang, Jan Kautz
Proceedings of the Computer Vision and Pattern
Recognition (CVPR) 2018 (Oral
Presentation + Best paper honorable mention award)
Abstract: We present a network
architecture for processing point clouds that directly
operates on the collection of points represented as a
sparse set of samples in a high-dimensional lattice.
Naively applying convolutions on this lattice scales
poorly both in terms of memory and computational cost as
the size of the lattice increases. Instead, our network
uses sparse bilateral convolutional layers as building
blocks. These layers maintain efficiency by using indexing
structures to apply convolutions only on occupied parts of
the lattice, and allow flexible specification of the
lattice structure enabling hierarchical and
spatially-aware feature learning, as well as joint 2D-3D
reasoning. Both point-based and image-based
representations can be easily incorporated in a network
with such layers and the resulting model can be trained in
an end-to-end manner. We present results on 3D
segmentation tasks where our approach outperforms existing
state-of-the-art techniques.
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Neural Shape Parsers for Constructive Solid
Geometry
[JOURNAL
PAPER - TPAMI] [CONFERENCE
PAPER - CVPR] [PAGE
WITH CODE & DATA]
Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji
Proceedings of the Computer Vision and Pattern
Recognition (CVPR) 2018
(an extended version also appeared at the IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI) 2022, vol. 44, no. 5)
Abstract: Constructive Solid Geometry
(CSG) is a geometric modeling technique that defines
complex shapes by recursively applying boolean operations
on primitives such as spheres and cylinders. We present
CSGNET, a deep network architecture that takes as input a
2D or 3D shape and outputs a CSG program that models it.
Parsing shapes into CSG programs is desirable as it yields
a compact and interpretable generative model. However, the
task is challenging since the space of primitives and
their combinations can be prohibitively large. CSGNET uses
a convolutional encoder and recurrent decoder based on
deep networks to map shapes to modeling instructions in a
feed-forward manner and is significantly faster than
bottom-up approaches. We investigate two architectures for
this task - a vanilla encoder (CNN) - decoder (RNN) and
another architecture that augments the encoder with an
explicit memory module based on the program execution
stack. The stack augmentation improves the reconstruction
quality of the generated shape and learning efficiency.
Our approach is also more effective as a shape primitive
detector compared to a state-of-the-art object detector.
Finally, we demonstrate CSGNET can be trained on novel
datasets without program annotations through policy
gradient techniques.
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Learning Material-Aware Local Descriptors for 3D
Shapes
[PAPER]
Hubert Lin, Melinos Averkiou, Evangelos Kalogerakis, Balazs Kovacs, Siddhant Ranade, Vladimir
Kim, Siddhartha Chaudhuri, Kavita Bala
Proceedings of the
International Conference on 3D Vision (3DV) 2018
Abstract: Material understanding is
critical for design, geometric modeling, and analysis of
functional objects. We enable material-aware 3D shape
analysis by employing a projective convolutional neural
network architecture to learn material-aware descriptors
from view-based representations of 3D points for
point-wise material classification or material-aware
retrieval. Unfortunately, only a small fraction of shapes
in 3D repositories are labeled with physical materials,
posing a challenge for learning methods. To address this
challenge, we crowdsource a dataset of 3080 3D shapes with
part-wise material labels. We focus on furniture models
which exhibit interesting structure and material
variability. In addition, we also contribute a
high-quality expert-labeled benchmark of 115 shapes from
Herman-Miller and IKEA for evaluation. We further apply a
mesh-aware conditional random field, which incorporates
rotational and reflective symmetries, to smooth our local
material predictions across neighboring surface patches.
We demonstrate the effectiveness of our learned
descriptors for automatic texturing, material-aware part
retrieval, and physical simulation.
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High Resolution Shape Completion Using Deep Neural
Networks for Global Structure and Local Geometry Inference
[PAPER][PAGE]
Xiaoguang Han*, Zhen Li*, Haibin Huang,
Evangelos Kalogerakis, Yizhou Yu
(* indicates equal contribution)
Proceedings of the International Conference on
Computer Vision (ICCV) 2017 (Selected for Spotlight Presentation)
Abstract: We propose a data-driven
method for recovering missing parts of 3D shapes. Our
method is based on a new deep learning architecture
consisting of two sub-networks: a global structure
inference network and a local geometry refinement network.
The global structure inference network incorporates a long
short-term memorized context fusion module (LSTM-CF) that
infers the global structure of the shape based on
multi-view depth information provided as part of the
input. It also includes a 3D fully convolutional (3DFCN)
module that further enriches the global structure
representation according to volumetric information in the
input. Under the guidance of the global structure network,
the local geometry refinement network takes as input local
3D patches around missing regions, and progressively
produces a high-resolution, complete surface through a
volumetric encoder-decoder architecture. Our method
jointly trains the global structure inference and local
geometry refinement networks in an end-to-end manner. We
perform qualitative and quantitative evaluations on six
object categories, demonstrating that our method
outperforms existing state-of-the-art work on shape
completion.
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3D Shape Reconstruction from Sketches via
Multi-view Convolutional Networks
[PAPER][PAGE
WITH CODE & DATA]
Zhaoliang Lun, Matheus Gadelha, Evangelos Kalogerakis, Subhransu Maji, Rui Wang
Proceedings of the International Conference on 3D
Vision (3DV) 2017 (Selected for Oral Presentation)
Abstract: We propose a method for
reconstructing 3D shapes from 2D sketches in the form of
line drawings. Our method takes as input a single sketch,
or multiple sketches, and outputs a dense point cloud
representing a 3D reconstruction of the input sketch(es).
The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder
network. The encoder converts the sketch into a compact
representation encoding shape information. The decoder
converts this representation into depth and normal maps
capturing the underlying surface from several output
viewpoints. The multi-view maps are then consolidated into
a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on
our experiments, compared to other methods, such as
volumetric networks, our architecture offers several
advantages, including more faithful reconstruction, higher
output surface resolution, better preservation of topology
and shape structure.
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Learning to Group Discrete Graphical Patterns
[PAPER][PAGE]
Zhaoliang Lun*, Changqing Zou*, Haibin Huang, Evangelos Kalogerakis, Ping Tan, Marie-Paule Cani, Hao
Zhang
(* indicates equal contribution)
ACM Transactions on Graphics, Vol.
36, No. 6, 2017
(also in the Proceedings of ACM SIGGRAPH ASIA
2017)
Abstract: We introduce a deep
learning approach for grouping discrete patterns common in
graphical designs. Our approach is based on a
convolutional neural network architecture that learns a
grouping measure defined over a pair of pattern elements.
Motivated by perceptual grouping principles, the key
feature of our network is the encoding of element shape,
context, symmetries, and structural arrangements. These
element properties are all jointly considered and
appropriately weighted in our grouping measure. To better
align our measure with the human perception of grouping,
we train our network on a large, human-annotated dataset
of pattern groupings consisting of patterns at varying
granularity levels, with rich element relations and
varieties, tempered with noise and other data
imperfections. Our results demonstrate that our
deep-learned measure leads to robust pattern groupings.
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3D Shape Segmentation with Projective
Convolutional Networks
[PAPER][PAGE WITH CODE
& DATA]
Evangelos Kalogerakis, Melinos Averkiou, Subhransu
Maji, Siddhartha Chaudhuri
Proceedings of the Computer Vision and Pattern
Recognition (CVPR) 2017 (Selected for Oral Presentation)
Abstract: This paper introduces a
deep architecture for segmenting 3D objects into their
labeled semantic parts. Our architecture combines
image-based Fully Convolutional Networks (FCNs) and
surface-based Conditional Random Fields (CRFs) to yield
coherent segmentations of 3D shapes. The image-based FCNs
are used for efficient view-based reasoning about 3D
object parts. Through a special projection layer, FCN
outputs are effectively aggregated across multiple views
and scales, then are projected onto the 3D object
surfaces. Finally, a surface-based CRF combines the
projected outputs with geometric consistency cues to yield
coherent segmentations. The whole architecture (multi-view
FCNs and CRF) is trained end-to-end. Our approach
significantly outperforms the existing state-of-the-art
methods in the currently largest segmentation benchmark
(ShapeNet). Finally, we demonstrate promising segmentation
results on noisy 3D shapes acquired from consumer-grade
depth cameras.
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Shape Synthesis from Sketches via Procedural
Models and Convolutional Networks
[PAPER][PAGE WITH CODE &
DATA]
Haibin Huang, Evangelos Kalogerakis, Ersin
Yumer, Radomir Mech
IEEE Transactions on Visualization and Computer
Graphics, Vol. 23, No. 8, 2017
(also in Pacific Graphics 2016)
Abstract: Procedural modeling
techniques can produce high quality visual content through
complex rule sets. However, controlling the outputs of
these techniques for design purposes is often notoriously
difficult for users due to the large number of parameters
involved in these rule sets and also their non-linear
relationship to the resulting content. To circumvent this
problem, we present a sketch-based approach to procedural
modeling. Given an approximate and abstract hand-drawn 2D
sketch provided by a user, our algorithm automatically
computes a set of procedural model parameters, which in
turn yield multiple, detailed output shapes that resemble
the user's input sketch. The user can then select an
output shape, or further modify the sketch to explore
alternative ones. At the heart of our approach is a deep
Convolutional Neural Network (CNN) that is trained to map
sketches to procedural model parameters. The network is
trained by large amounts of automatically generated
synthetic line drawings. By using an intuitive medium
i.e., freehand sketching as input, users are set free from
manually adjusting procedural model parameters, yet they
are still able to create high quality content. We
demonstrate the accuracy and efficacy of our method in a
variety of procedural modeling scenarios including design
of man-made and organic shapes.
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Data-Driven Shape Analysis and Processing
[PAPER][PAGE WITH CODE
& DATA]
Kai Xu, Vladimir Kim, Qixing Huang, Evangelos Kalogerakis
Computer Graphics Forum, Vol. 36,
No. 1, 2017
(also in Eurographics 2016)
Abstract: Data-driven methods play an
increasingly important role in discovering geometric,
structural, and semantic relationships between 3D shapes
in collections, and applying this analysis to support
intelligent modeling, editing, and visualization of
geometric data. In contrast to traditional approaches, a
key feature of data-driven approaches is that they
aggregate information from a collection of shapes to
improve the analysis and processing of individual shapes.
In addition, they are able to learn models that reason
about properties and relationships of shapes without
relying on hard-coded rules or explicitly programmed
instructions. We provide an overview of the main concepts
and components of these techniques, and discuss their
application to shape classification, segmentation,
matching, reconstruction, modeling and exploration, as
well as scene analysis and synthesis, through reviewing
the literature and relating the existing works with both
qualitative and numerical comparisons. We conclude our
report with ideas that can inspire future research in
data-driven shape analysis and processing.
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Functionality Preserving Shape Style Transfer
[PAPER][VIDEO][PAGE
WITH CODE & DATA]
Zhaoliang Lun, Evangelos Kalogerakis, Rui
Wang, Alla Sheffer
ACM Transactions on Graphics, Vol.
35, No. 6, 2016
(also in the Proceedings of ACM SIGGRAPH ASIA
2016)
Abstract: When geometric models with
a desired combination of style and functionality are not
available, they currently need to be created manually. We
facilitate algorithmic synthesis of 3D models of man-made
shapes which combines user-specified style, described via
an exemplar shape, and functionality, encoded by a
functionally different target shape. Our method
automatically transfers the style of the exemplar to the
target, creating the desired combination. The main
challenge in performing cross-functional style transfer is
to implicitly separate an object's style from its
function: while stylistically the output shapes should be
as close as possible to the exemplar, their original
functionality and structure, as encoded by the target,
should be strictly preserved. Recent literature point to
the presence of similarly shaped, salient geometric
elements as a main indicator of stylistic similarity
between 3D shapes. We therefore transfer the exemplar
style to the target via a sequence of element-level
operations. We allow only compatible operations, ones that
do not affect the target functionality. To this end, we
introduce a cross-structural element compatibility metric
that estimates the impact of each operation on the edited
shape. Our metric is based on the global context and
coarse geometry of evaluated elements, and is trained on
databases of 3D objects. We use this metric to cast style
transfer as a tabu search, which incrementally updates the
target shape using compatible operations, progressively
increasing its style similarity to the exemplar while
strictly maintaining its functionality at each step. We
evaluate our framework across a range of man-made objects
including furniture, light fixtures, and tableware, and
perform a number of user studies confirming that it
produces convincing outputs combining the desired style
and function.
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Direct shape optimization for strengthening
3D printable objects
[PAPER][VIDEO][PAGE
WITH CODE & DATA]
Yahan Zhou, Evangelos Kalogerakis, Rui
Wang, Ian R. Grosse
Computer Graphics Forum, Vol. 35, No. 7, 2016
(also in Pacific Graphics 2016)
Abstract: Recently there has been an
increasing demand for software that can help designers
create functional 3D objects with required physical
strength. We introduce a generic and extensible method
that directly optimizes a shape subject to physical and
geometric constraints. Given an input shape, our method
optimizes directly its input mesh representation until it
can withstand specified external forces, while remaining
similar to the original shape. Our method performs physics
simulation and shape optimization together in a unified
framework, where the physics simulator is an integral part
of the optimizer. We employ geometric constraints to
preserve surface details and shape symmetry, and adapt a
second-order method with analytic gradients to improve
convergence and computation time. Our method provides
several advantages over previous work, including the
ability to handle general shape deformations, preservation
of surface details, and incorporation of user-defined
constraints. We demonstrate the effectiveness of our
method on a variety of printable 3D objects through
detailed simulations as well as physical validations.
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Multi-view Convolutional Neural Networks for
3D Shape Recognition
[PAPER][VIDEO][PAGE WITH
CODE & DATA]
Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller
Proceedings of the International Conference on
Computer Vision (ICCV) 2015
Winner of the SHREC 2016
Large-Scale 3D Shape Retrieval Competition (normal
dataset) [SHREC
2016 PAPER]
Abstract: A longstanding question in
computer vision concerns the representation of 3D shapes
for recognition: should 3D shapes be represented with
descriptors operating on their native 3D formats, such as
voxel grid or polygon mesh, or can they be effectively
represented with view-based descriptors? We address this
question in the context of learning to recognize 3D shapes
from a collection of their rendered views on 2D images. We
first present a standard CNN architecture trained to
recognize the shapes’ rendered views independently of each
other, and show that a 3D shape can be recognized even
from a single view at an accuracy far higher than using
state-of-the-art 3D shape descriptors. Recognition rates
further increase when multiple views of the shapes are
provided. In addition, we present a novel CNN architecture
that combines information from multiple views of a 3D
shape into a single and compact shape descriptor offering
even better recognition performance. The same architecture
can be applied to accurately recognize human hand-drawn
sketches of shapes. We conclude that a collection of 2D
views can be highly informative for 3D shape recognition
and is amenable to emerging CNN architectures and their
derivatives.
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Elements of Style: Learning Perceptual Shape
Style Similarity
[PAPER][VIDEO][PAGE
WITH CODE & DATA]
Zhaoliang Lun, Evangelos Kalogerakis, Alla
Sheffer
ACM Transactions on Graphics, Vol. 34, No. 4,
2015
(also in the Proceedings of ACM SIGGRAPH 2015)
Abstract: The human perception of
stylistic similarity transcends structure and function:
for instance, a bed and a dresser may share a common
style. An algorithmically computed style similarity
measure that mimics human perception can benefit a range
of computer graphics applications. Previous work in style
analysis focused on shapes within the same class, and
leveraged structural similarity between these shapes to
facilitate analysis. In contrast, we introduce the first
structure-transcending style similarity measure and
validate it to be well aligned with human perception of
stylistic similarity. Our measure is inspired by
observations about style similarity in art history
literature, which point to the presence of similarly
shaped, salient, geometric elements as one of the key
indicators of stylistic similarity. We translate these
observations into an algorithmic measure by first
quantifying the geometric properties that make humans
perceive geometric elements as similarly shaped and
salient in the context of style, then employing this
quantification to detect pairs of matching style related
elements on the analyzed models, and finally collating the
element-level geometric similarity measurements into an
object-level style measure consistent with human
perception. To achieve this consistency we employ
crowdsourcing to quantify the different components of our
measure; we learn the relative perceptual importance of a
range of elementary shape distances and other parameters
used in our measurement from 50K responses to
cross-structure style similarity queries provided by over
2500 participants. We train and validate our method on
this dataset, showing it to successfully predict relative
style similarity with near 90% accuracy based on 10-fold
cross-validation.
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Analysis and synthesis of 3D shape families
via deep-learned generative models of surfaces
[PAPER][PAGE WITH CODE & DATA]
Haibin Huang, Evangelos Kalogerakis,
Benjamin Marlin
Computer Graphics Forum, Vol. 34, No. 5, 2015
(also in the Proceedings of SGP 2015)
Abstract: We present a method for
joint analysis and synthesis of geometrically diverse 3D
shape families. Our method first learns part-based
templates such that an optimal set of fuzzy point and part
correspondences is computed between the shapes of an input
collection based on a probabilistic deformation model. In
contrast to previous template-based approaches, the
geometry and deformation parameters of our part-based
templates are learned from scratch. Based on the estimated
shape correspondence, our method also learns a
probabilistic generative model that hierarchically
captures statistical relationships of corresponding
surface point positions and parts as well as their
existence in the input shapes. A deep learning procedure
is used to capture these hierarchical relationships. The
resulting generative model is used to produce control
point arrangements that drive shape synthesis by combining
and deforming parts from the input collection. The
generative model also yields compact shape descriptors
that are used to perform fine-grained classification.
Finally, it can be also coupled with the probabilistic
deformation model to further improve shape correspondence.
We provide qualitative and quantitative evaluations of our
method for shape correspondence, segmentation,
fine-grained classification and synthesis. Our experiments
demonstrate superior correspondence and segmentation
results than previous state-of-the-art approaches.
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RisQ: Recognizing Smoking Gestures with
Inertial Sensors on a Wristband
[PAPER]
Abhinav Parate, Meng-Chieh Chiu, Chaniel
Chadowitz, Deepak Ganesan, Evangelos Kalogerakis
Proceedings of ACM MobiSys 2014
Abstract: Smoking-induced diseases
are known to be the leading cause of death in the United
States. In this work, we design RisQ, a mobile solution
that leverages a wristband containing a 9-axis inertial
measurement unit to capture changes in the orientation of
a person’s arm, and a machine learning pipeline that
processes this data to accurately detect smoking gestures
and sessions in real-time. Our key innovations are
fourfold: a) an arm trajectory-based method that extracts
candidate hand-to-mouth gestures, b) a set of
trajectory-based features to distinguish smoking gestures
from confounding gestures including eating and drinking,
c) a probabilistic model that analyzes sequences of
hand-to-mouth gestures and infers which gestures are part
of individual smoking sessions, and d) a method that
leverages multiple IMUs placed on a person’s body together
with 3D animation of a person’s arm to reduce burden of
self-reports for labeled data collection. Our experiments
show that our gesture recognition algorithm can detect
smoking gestures with high accuracy (95.7%), precision
(91%) and recall (81%). We also report a user study that
demonstrates that we can accurately detect the number of
smoking sessions with very few false positives over the
period of a day, and that we can reliably extract the
beginning and end of smoking session periods.
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Analogy-Driven 3D Style Transfer
[PAPER][VIDEO][PAGE]
Chongyang Ma, Haibin Huang, Alla Sheffer, Evangelos Kalogerakis, Rui Wang
Computer Graphics Forum, Vol. 33, No. 2,
2014
(also in the Proceedings of Eurographics 2014)
Abstract: Style transfer aims to
apply the style of an exemplar model to a target one,
while retaining the target’s structure. The main challenge
in this process is to algorithmically distinguish style
from structure, a high-level, potentially ill-posed
cognitive task. We recast style transfer in terms of shape
analogies. We use the proposed framework to seamlessly
transfer a variety of style properties between 2D and 3D
objects and demonstrate significant improvements over the
state of the art in style transfer. We further show that
our framework can be used to successfully complete partial
scans with the help of a user provided structural
template, coherently propagating scan style across the
completed surfaces.
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AttribIt: Content Creation with Semantic
Attributes
[PAPER][VIDEO][PAGE]
Siddhartha Chaudhuri*, Evangelos
Kalogerakis*, Stephen Giguere, Thomas
Funkhouser
(*S. Chaudhuri and E. Kalogerakis contributed equally to
this work)
Proceedings of the ACM UIST 2013
conference
Abstract: We present ATTRIBIT,
an approach for people to create visual content using
relative semantic attributes expressed in linguistic
terms. During an off-line processing step, ATTRIBIT
learns semantic attributes for design components that
reflect the high-level intent people may have for creating
content in a domain (e.g., adjectives such as “dangerous,”
“scary,” or “”) and ranks them according to the
strength of each learned attribute. Then, during an
interactive design session, a person can explore different
combinations of visual components using commands based on
relative attributes (e.g. “make this part more
dangerous”). Novel designs are assembled in real-time as
the strength of selected attributes are varied, enabling
rapid, in-situ exploration of candidate designs. We
applied this approach to 3D modeling and web design.
Experiments suggest this interface is an effective
alternative for novices performing tasks with high-level
design goals.
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Implicit Integration for Particle-based
Simulation of Elasto-plastic Solids
[PAPER][VIDEO]
Yahan Zhou, Zhaoliang Lun, Evangelos Kalogerakis, and
Rui Wang
Computer Graphics Forum, Vol. 32, No.
7, 2013
(also in the Proceedings of Pacific Graphics 2013)
Abstract: We present a novel
particle-based method for stable simulation of
elasto-plastic materials. The main contribution of our
method is an implicit numerical integrator, using a
physically-based model, for computing particles that
undergo both elastic and plastic deformations. The main
advantage of our implicit integrator is that it allows the
use of large time steps while still preserving stable and
physically plausible simulation results. As a key
component of our algorithm, at each time step we compute
the particle positions and velocities based on a sparse
linear system, which we solve efficiently on the graphics
hardware. Compared to existing techniques, our method
allows for a much wider range of stiffness and plasticity
settings. In addition, our method can significantly reduce
the computation cost for certain range of material types.
We demonstrate fast and stable simulations for a variety
of elasto-plastic materials, ranging from highly stiff
elastic materials to highly plastic ones.
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A Probabilistic Model for Component-Based
Shape Synthesis
[PAPER][VIDEO][PAGE WITH CODE
& DATA]
Evangelos Kalogerakis, Siddhartha Chaudhuri, Daphne
Koller, Vladlen Koltun
ACM Transactions on Graphics, Vol. 31,
No. 4, 2012
(also in the
Proceedings of SIGGRAPH 2012)
Abstract: We present an approach
to synthesizing shapes from complex domains, by
identifying new plausible combinations of components
from existing shapes. Our primary contribution is a new
generative model of component-based shape structure. The
model represents probabilistic relationships between
properties of shape components, and relates them to
learned underlying causes of structural variability
within the domain. These causes are treated as latent
variables, leading to a compact representation that can
be effectively learned without supervision from a set of
compatibly segmented shapes. We evaluate the model on a
number of shape datasets with complex structural
variability and demonstrate its application to
amplification of shape databases and to interactive
shape synthesis.
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Learning Hatching for Pen-and-Ink
Illustration of Surfaces
[PAPER][PAGE]
Evangelos Kalogerakis, Derek Nowrouzezahrai, Simon
Breslav, Aaron Hertzmann
ACM Transactions on Graphics, Vol. 31,
No. 1, 2012
(also in SIGGRAPH 2012)
Abstract: This paper presents an
algorithm for learning hatching styles from line
drawings. An artist draws a single hatching illustration
of a 3D object. Their strokes are analyzed to extract
the following per-pixel properties: hatching level
(hatching, cross-hatching, or no strokes), stroke
orientation, spacing, intensity, length, and thickness.
A mapping is learned from input features to these
properties, using classification, regression, and
clustering techniques. Then, a new illustration can be
generated in the artist’s style, as follows. First,
given a new view of a 3D object, the learned mapping is
applied to synthesize target stroke properties for each
pixel. A new illustration is then generated by
synthesizing hatching strokes according to the target
properties.
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Probabilistic Reasoning for Assembly-Based 3D
Modeling
[PAPER][VIDEO]
Siddhartha Chaudhuri*, Evangelos Kalogerakis*, Leonidas Guibas,
Vladlen Koltun
(*S. Chaudhuri and E. Kalogerakis
contributed equally to this work)
ACM Transactions on Graphics, Vol. 30,
No. 4, 2011
(also in the Proceedings
of SIGGRAPH 2011)
Abstract: Assembly-based modeling
is a promising approach to broadening the accessibility
of 3D modeling. In assembly-based modeling, new models
are assembled from shape components extracted from a
database. A key challenge in assembly-based modeling is
the identification of relevant components to be
presented to the user. In this paper, we introduce a
probabilistic reasoning approach to this problem. Given
a repository of shapes, our approach learns a
probabilistic graphical model that encodes semantic and
geometric relationships among shape components. The
probabilistic model is used to present components that
are semantically and stylistically compatible with the
3D model that is being assembled. Our experiments
indicate that the probabilistic model increases the
relevance of presented components.
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Learning 3D Mesh Segmentation and Labeling
[PAPER]
[PAGE
WITH CODE & DATA]
Evangelos Kalogerakis, Aaron Hertzmann, Karan Singh
ACM Transactions on Graphics, Vol. 29, No.
3, 2010
(also in the Proceedings of SIGGRAPH 2010)
Abstract: This paper presents a
data-driven approach to simultaneous segmentation and
labeling of parts in 3D meshes. An objective function is
formulated as a Conditional Random Field model, with
terms assessing the consistency of faces with labels,
and terms between labels of neighboring faces. The
objective function is learned from a collection of
labeled training meshes. The algorithm uses hundreds of
geometric and contextual label features and learns
different types of segmentations for different tasks,
without requiring manual parameter tuning. Our algorithm
achieves a significant improvement in results over the
state-of-the-art when evaluated on the Princeton
Segmentation Benchmark, often producing segmentations
and labelings comparable to those produced by humans.
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Image Sequence Geolocation with Human Travel
Priors
[PAPER]
[PAGE]
Evangelos Kalogerakis, Olga Vesselova, James Hays,
Alexei Efros, Aaron Hertzmann
Proceedings of the
International Conference on Computer Vision (ICCV)
2009 (Selected for Oral
Presentation)
Abstract: This
paper presents a method for estimating geographic
location for sequences of time-stamped photographs. A
prior distribution over travel describes the likelihood
of traveling from one location to another during a given
time interval. This distribution is based on a training
database of 6 million photographs from Flickr.com. An
image likelihood for each location is defined by
matching a test photograph against the training
database. Inferring location for images in a test
sequence is then performed using the Forward-Backward
algorithm, and the model can be adapted to individual
users as well. Using temporal constraints allows our
method to geolocate images without recognizable
landmarks, and images with no geographic cues
whatsoever. This method achieves a substantial
performance improvement over the best-available
baseline, and geolocates some users’ images with
near-perfect accuracy.
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Data-driven curvature for real-time line
drawing of dynamic scenes
[PAPER][VIDEO][PAGE]
Evangelos Kalogerakis, Derek Nowrouzezahrai,
Patricio Simari, James McCrae, Aaron Hertzmann, Karan
Singh
ACM Transactions on
Graphics, Vol. 28, No. 1, 2009
(also in SIGGRAPH 2009)
Abstract: This paper presents a
method for real-time line drawing of deforming objects.
Object-space line drawing algorithms for many types of
curves, including suggestive contours, highlights,
ridges and valleys, rely on surface curvature and
curvature derivatives. Unfortunately, these curvatures
and their derivatives cannot be computed in real-time
for animated, deforming objects. In a preprocessing
step, our method learns the mapping from a
low-dimensional set of animation parameters to surface
curvatures for a deforming 3D mesh. The learned model
can then accurately and efficiently predict curvatures
and their derivatives, enabling real-time object-space
rendering of suggestive contours and other such curves.
This represents an order-of-magnitude speed-up over the
fastest existing algorithm capable of estimating
curvatures and their derivatives accurately enough for
many different types of line drawings. The learned model
can generalize to novel animation sequences, and is also
very compact, requiring a few megabytes of storage. We
demonstrate our method for various types of animated
objects, including skeleton-based characters, cloth
simulation and facial animation, using a variety of
non-photorealistic rendering styles.
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Multi-objective shape segmentation and
labeling
[PAPER]
[VIDEO]
Patricio Simari, Derek Nowrouzezahrai, Evangelos Kalogerakis, Karan Singh
Computer Graphics Forum, Vol. 28, No. 5,
2009
(also in the Proceedings of EG Symposium
of Geometry Processing 2009)
Abstract: In this paper, we
perform segmentation and labeling of shapes based on a
simultaneous optimization of multiple heterogenous
objectives that capture application-specific
segmentation criteria. We present a number of efficient
objective functions that capture useful shape adjectives
(compact, flat, narrow, perpendicular, etc.)
Segmentation descriptions within our framework combine
multiple such objective functions with optional labels
to define each part. The optimization problem is
simplified by proposing weighted Voronoi partitioning as
a compact and continuous parametrization of spatially
embedded shape segmentations. This partition is
automatically labeled to optimize heterogeneous part
objectives and the Voronoi centers and their weights
optimized using Generalized Pattern Search. We
illustrate our framework using several diverse
segmentation applications: bounding volume hierarchies
for path tracing, and automatic rig and clothing
transfer between animation characters.
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[animation
dataset by Joel Anderson ©] |
Shadowing Dynamic Scenes with Arbitrary
BRDFs
[PAPER]
[VIDEO]
Derek Nowrouzezahrai, Evangelos Kalogerakis, Eugene
Fiume
Computer Graphics Forum,
Vol. 28, No. 2, 2009
(also in the Proceedings
of Eurographics 2009
Abstract:
We present a real-time relighting and shadowing method
for dynamic scenes with varying lighting, view and
BRDFs. Our approach is based on a compact representation
of reflectance data that allows for changing the BRDF at
run-time and a data-driven method for accurately
synthesizing self-shadows on articulated and deformable
geometries. Unlike previous self-shadowing approaches,
we do not rely on local blocking heuristics. We do not
fit a model to the BRDF-weighted visibility, but rather
only to the visibility that changes during animation. In
this manner, our model is more compact than previous
techniques and requires less computation both during
fitting and at run-time. Our reflectance product
operators can re-integrate arbitrary low-frequency
view-dependent BRDF effects on-the-fly and are
compatible with all previous dynamic visibility
generation techniques as well as our own data-driven
visibility model. We apply our reflectance product
operators to three different visibility generation
models, and our data-driven model can achieve framerates
well over 300Hz.
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Extracting lines of curvature from noisy
point clouds
[PAPER]
[PAGE]
Evangelos Kalogerakis, Derek Nowrouzezahrai,
Patricio Simari, Karan Singh
Special Issue of the Computer-Aided Design on
Point-Based Computational Techniques, Vol. 41, No. 4,
2009
Abstract: We present a robust
framework for extracting lines of curvature from point
clouds. First, we show a novel approach to denoising the
input point cloud using robust statistical estimates of
surface normal and curvature which automatically rejects
outliers and corrects points by energy minimization.
Then the lines of curvature are constructed on the point
cloud with controllable density. Our approach is
applicable to surfaces of arbitrary genus, with or
without boundaries, and is statistically robust to noise
and outliers while preserving sharp surface features. We
show our approach to be eective over a range of
synthetic and real-world input datasets with varying
amounts of noise and outliers. The extraction of
curvature information can benefit many applications in
CAD, computer vision and graphics for point cloud shape
analysis, recognition and segmentation. Here, we show
the possibility of using the lines of curvature for
feature-preserving mesh construction directly from noisy
point clouds.
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Eigentransport for Efficient
and Accurate All-Frequency Relighting
[PAPER]
Derek Nowrouzezahrai, Patricio Simari, Evangelos Kalogerakis, Eugene Fiume
Proceedings of ACM Graphite 2007 (Best
Paper Award)
Abstract: We present a method for
creating a geometry-dependent basis for precomputed
radiance transfer. Unlike previous PRT bases, ours is
derived from principal component analysis of the sampled
transport functions at each vertex. It allows for
efficient evaluation of shading, has low memory
requirements and produces accurate results with few
coefficients. We are able to capture all-frequency effects
from both distant and near-field dynamic lighting in
real-time and present a simple rotation scheme.
Reconstruction of the final shading becomes a low-order
dot product and is performed on the GPU.
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Robust statistical
estimation of curvature on discretized surfaces
[PAPER]
[PAGE WITH
EXECUTABLE]
Evangelos Kalogerakis, Patricio Simari, Derek
Nowrouzezahrai, Karan Singh
Proceedings of EG Symposium on Geometry Processing
2007
Abstract: A robust statistics
approach to curvature estimation on discretely sampled
surfaces, namely polygon meshes and point clouds, is
presented. The method exhibits accuracy, stability and
consistency even for noisy, non-uniformly sampled surfaces
with irregular configurations. Within an M-estimation
framework, the algorithm is able to reject noise and
structured outliers by sampling normal variations in an
adaptively reweighted neighborhood around each point. The
algorithm can be used to reliably derive higher order
differential attributes and even correct noisy surface
normals while preserving the fine features of the normal
and curvature field. The approach is compared with
state-of-the-art curvature estimation methods and shown to
improve accuracy by up to an order of magnitude across
ground truth test surfaces under varying tessellation
densities and types as well as increasing degrees of
noise. Finally, the benefits of a robust statistical
estimation of curvature are illustrated by applying it to
the popular applications of mesh segmentation and
suggestive contour rendering.
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Folding meshes: Hierarchical
mesh segmentation based on planar symmetry
[PAPER]
Patricio Simari, Evangelos Kalogerakis, Karan Singh
Proceedings of EG Symposium
on Geometry Processing 2006
Abstract: Meshes representing real
world objects, both artist-created and scanned, contain a
high level of redundancy due to approximate planar
reflection symmetries, either global or localized to
different subregions. An algorithm is presented for
detecting such symmetries and segmenting the mesh into the
symmetric and remaining regions. The method has
foundations in robust statistics and is resilient to
structured outliers which are present in the form of the
non symmetric regions of the data. Also introduced is an
application of the method: the folding tree data
structure. The structure encodes the non redundant regions
of the original mesh as well as the reflection planes and
is created by the recursive application of the detection
method. This structure can then be unfolded to recover the
original shape. Applications include mesh compression,
repair as well as mesh processing acceleration by limiting
computation to non redundant regions and propagation of
results.
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Coupling ontologies with
graphics content for Knowledge Driven Visualization
[PAPER]
[PAGE]
Evangelos Kalogerakis, Nektarios Moumoutzis, Stavros
Christodoulakis
Proceedings of IEEE Virtual Reality 2006
Abstract: A great challenge in
information visualization today is to provide models and
software that effectively integrate the graphics content
of scenes with domain-specific knowledge so that the users
can effectively query, interpret, personalize and
manipulate the visualized information. Moreover, it is
important that such applications are interoperable in the
semantic web environment and thus, require that the models
and software supporting them integrate state-of-the-art
international standards for knowledge representation,
graphics and multimedia. In this paper, we present a model
and a software framework for the semantic web for the
development of interoperable intelligent visualization
applications that support the coupling of graphics and
virtual reality scenes with domain knowledge of different
domains. We also provide methods for knowledge driven
information visualization and visualization-aided decision
making based on inference by reasoning.
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