Siddhartha Chaudhuri
Most Recent Affiliation(s):
- Adobe Research, IIT Bombay, Stanford University, Princeton University
Learning Category: Jury Member:
Learning Category: Presentation(s):
![Fast Complementary Dynamics via Skinning Eigenmodes](https://history.siggraph.org/wp-content/uploads/2024/02/2023-Tech-Papers-Benchekroun_Fast-Complementary-Dynamics-via-Skinning-Eigenmodes-150x150.jpg)
Type: [Technical Papers]
Fast Complementary Dynamics via Skinning Eigenmodes Presenter(s): [Benchekroun] [Zhang] [Chaudhuri] [Grinspun] [Zhou] [Jacobson]
[SIGGRAPH 2023]
![Neural jacobian fields: learning intrinsic mappings of arbitrary meshes](https://history.siggraph.org/wp-content/uploads/2023/06/2022-Technical-Papers-Aigerman_-Neural-Jacobian-Fields-Learning-Intrinsic-Mappings-of-Arbitrary-Meshes-150x150.jpg)
Type: [Technical Papers]
Neural jacobian fields: learning intrinsic mappings of arbitrary meshes Presenter(s): [Aigerman] [Gupta] [Kim] [Chaudhuri] [Saito] [Groueix]
[SIGGRAPH 2022]
![Neural subdivision](https://history.siggraph.org/wp-content/uploads/2024/04/2020-Posters-Liu_Neural-Subdivision-05-150x150.jpg)
Type: [Posters]
Neural subdivision Presenter(s): [Liu] [Kim] [Chaudhuri] [Aigerman] [Jacobson]
[SIGGRAPH 2020]
![Neural Subdivision](https://history.siggraph.org/wp-content/uploads/2023/02/2020-Technical-Papers-Liu_Neural-Subdivision-150x150.jpg)
Type: [Technical Papers]
Neural Subdivision Presenter(s): [Liu] [Kim] [Chaudhuri] [Aigerman] [Jacobson]
[SIGGRAPH 2020]
![GRAINS: Generative Recursive Autoencoders for INdoor Scenes](https://history.siggraph.org/wp-content/uploads/2022/07/2019-SIGGRAPH-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
GRAINS: Generative Recursive Autoencoders for INdoor Scenes Presenter(s): [Li] [Patil] [Xu] [Chaudhuri] [Khan] [Shamir] [Tu] [Chen] [Cohen-Or] [Zhang]
[SIGGRAPH 2019]
![Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks](https://history.siggraph.org/wp-content/uploads/2022/07/2018-SIGGRAPH-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks Presenter(s): [Huang] [Kalogerakis] [Chaudhuri] [Ceylan] [Kim]
[SIGGRAPH 2018]
![GRASS: generative recursive autoencoders for shape structures](https://history.siggraph.org/wp-content/uploads/2023/02/2017-Technical-Papers-Li_GRASS_-Generative-Recursive-Autoencoders-for-Shape-Structures-150x150.jpg)
Type: [Technical Papers]
GRASS: generative recursive autoencoders for shape structures Presenter(s): [Liu] [Xu] [Chaudhuri] [Yumer] [Zhang] [Guibas]
[SIGGRAPH 2017]
![Semantic shape editing using deformation handles](https://history.siggraph.org/wp-content/uploads/2023/04/2015-Technichal-Papers-Yumer_Semantic-Shape-Editing-Using-Deformation-Handles-150x150.jpg)
Type: [Technical Papers]
Semantic shape editing using deformation handles Presenter(s): [Yumer] [Chaudhuri] [Hodgins] [Kara]
[SIGGRAPH 2015]
![Shape2Pose: human-centric shape analysis](https://history.siggraph.org/wp-content/uploads/2023/02/2014-Technical-Papers-Kim_Shape2Pose-Human-Centric-Shape-Analysis-150x150.jpg)
Type: [Technical Papers]
Shape2Pose: human-centric shape analysis Presenter(s): [Kim] [Chaudhuri] [Guibas] [Funkhouser]
[SIGGRAPH 2014]
![Learning part-based templates from large collections of 3D shapes](https://history.siggraph.org/wp-content/uploads/2022/07/2013-SIGGRAPH-Image-Not-Available-150x150.jpg)
Type: [Technical Papers]
Learning part-based templates from large collections of 3D shapes Presenter(s): [Kim] [Li] [Mitra] [Chaudhuri] [DiVerdi] [Funkhouser]
[SIGGRAPH 2013]
Role(s):
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