no code implementations • 20 Mar 2024 • R. Kenny Jones, Siddhartha Chaudhuri, Daniel Ritchie
We develop a learning paradigm that allows us to train networks that infer Template Programs directly from visual datasets that contain concept groupings.
no code implementations • 5 Feb 2024 • Rio Aguina-Kang, Maxim Gumin, Do Heon Han, Stewart Morris, Seung Jean Yoo, Aditya Ganeshan, R. Kenny Jones, Qiuhong Anna Wei, Kailiang Fu, Daniel Ritchie
Unlike most prior work on indoor scene generation, our system does not require a large training dataset of existing 3D scenes.
no code implementations • ICCV 2023 • Aditya Ganeshan, R. Kenny Jones, Daniel Ritchie
Programs offer compactness and structure that makes them an attractive representation for visual data.
1 code implementation • 9 May 2023 • R. Kenny Jones, Paul Guerrero, Niloy J. Mitra, Daniel Ritchie
The discovered abstractions capture common patterns (both structural and parametric) across the dataset, so that programs rewritten with these abstractions are more compact, and expose fewer degrees of freedom.
1 code implementation • 7 Jun 2022 • R. Kenny Jones, Aalia Habib, Daniel Ritchie
We present SHRED, a method for 3D SHape REgion Decomposition.
no code implementations • 13 Dec 2021 • Bryce Blinn, Alexander Ding, R. Kenny Jones, Manolis Savva, Srinath Sridhar, Daniel Ritchie
The body-shape-conditioned models produce chairs which will be comfortable for a person with the given body shape; the pose-conditioned models produce chairs which accommodate the given sitting pose.
1 code implementation • CVPR 2022 • R. Kenny Jones, Aalia Habib, Rana Hanocka, Daniel Ritchie
We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape.
1 code implementation • 13 Apr 2021 • R. Kenny Jones, David Charatan, Paul Guerrero, Niloy J. Mitra, Daniel Ritchie
In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs.
1 code implementation • CVPR 2022 • R. Kenny Jones, Homer Walke, Daniel Ritchie
Related to these approaches, we introduce a novel self-training variant unique to program inference, where program pseudo-labels are paired with their executed output shapes, avoiding label mismatch at the cost of an approximate shape distribution.
1 code implementation • 17 Sep 2020 • R. Kenny Jones, Theresa Barton, Xianghao Xu, Kai Wang, Ellen Jiang, Paul Guerrero, Niloy J. Mitra, Daniel Ritchie
The program captures the subset of variability that is interpretable and editable.