Search Results for author:
Found 10 papers, 6 papers with code
Learning to Infer Generative Template Programs for Visual Concepts
We develop a learning paradigm that allows us to train networks that infer Template Programs directly from visual datasets that contain concept groupings.
Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases
Unlike most prior work on indoor scene generation, our system does not require a large training dataset of existing 3D scenes.
Improving Unsupervised Visual Program Inference with Code Rewriting Families
Programs offer compactness and structure that makes them an attractive representation for visual data.
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives
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.
SHRED: 3D Shape Region Decomposition with Learned Local Operations
We present SHRED, a method for 3D SHape REgion Decomposition.
Learning Body-Aware 3D Shape Generative Models
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.
The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference
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.
ShapeMOD: Macro Operation Discovery for 3D Shape Programs
In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs.
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
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.
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
The program captures the subset of variability that is interpretable and editable.
