79 citations · 338 across the 27 of their papers we have counts for
27 papers
Improving Unsupervised Visual Program Inference with Code Rewriting Families
Aditya Ganeshan, R. Kenny Jones, Daniel Ritchie
Programs offer compactness and structure that makes them an attractive representation for visual data. We explore how code rewriting can be used to improve systems for inferring pr…
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives
R. Kenny Jones, Paul Guerrero, Niloy J. Mitra +1
Programs are an increasingly popular representation for visual data, exposing compact, interpretable structure that supports manipulation. Visual programs are usually written in do…
Neurosymbolic Models for Computer Graphics
Daniel Ritchie, Paul Guerrero, R. Kenny Jones +4
Procedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They o…
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher +2
Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes…
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu +5
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We intr…
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Rao Fu, Xiao Zhan, Yiwen Chen +2
We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt…