79 citations · 109 across the 13 of their papers we have counts for
13 papers
Procedural Scene Programs for Open-Universe Scene Generation: LLM-Free Error Correction via Program Search
Maxim Gumin, Do Heon Han, Seung Jean Yoo +6
Synthesizing 3D scenes from open-vocabulary text descriptions is a challenging, important, and recently-popular application. One of its critical subproblems is layout generation: g…
PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
Junyu Liu, R. Kenny Jones, Daniel Ritchie
We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful…
Imperative vs. Declarative Programming Paradigms for Open-Universe Scene Generation
Maxim Gumin, Do Heon Han, Seung Jean Yoo +5
Current methods for generating 3D scene layouts from text predominantly follow a declarative paradigm, where a Large Language Model (LLM) specifies high-level constraints that are…
ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Models
R. Kenny Jones, Paul Guerrero, Niloy J. Mitra +1
We present ShapeLib, the first method that uses the priors of Large Language Models (LLMs) to design libraries of programmatic 3D shape abstractions. Our system accepts two forms o…
Learning to Edit Visual Programs with Self-Supervision
R. Kenny Jones, Renhao Zhang, Aditya Ganeshan +1
We design a system that learns how to edit visual programs. Our edit network consumes a complete input program and a visual target. From this input, we task our network with predic…
ParSEL: Parameterized Shape Editing with Language
Aditya Ganeshan, Ryan Y. Huang, Xianghao Xu +2
The ability to edit 3D assets from natural language presents a compelling paradigm to aid in the democratization of 3D content creation. However, while natural language is often ef…
Learning to Infer Generative Template Programs for Visual Concepts
R. Kenny Jones, Siddhartha Chaudhuri, Daniel Ritchie
People grasp flexible visual concepts from a few examples. We explore a neurosymbolic system that learns how to infer programs that capture visual concepts in a domain-general fash…
Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases
Rio Aguina-Kang, Maxim Gumin, Do Heon Han +7
We present a system for generating indoor scenes in response to text prompts. The prompts are not limited to a fixed vocabulary of scene descriptions, and the objects in generated…
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…
SHRED: 3D Shape Region Decomposition with Learned Local Operations
R. Kenny Jones, Aalia Habib, Daniel Ritchie
We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a 3D point cloud as input and uses learned local operations to produce a segmentation that approximates fi…
ShapeMOD: Macro Operation Discovery for 3D Shape Programs
R. Kenny Jones, David Charatan, Paul Guerrero +2
A popular way to create detailed yet easily controllable 3D shapes is via procedural modeling, i.e. generating geometry using programs. Such programs consist of a series of instruc…
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
R. Kenny Jones, Theresa Barton, Xianghao Xu +5
Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: the…