2 citations · 3 across the 12 of their papers we have counts for
13 papers
Synthesizing Reactive Character Behaviors for Continuous Games via Programmatic Policy Search
Maxim Gumin, Hsueh-Ti Derek Liu, Victor Zordan +1
We present a method for synthesizing reactive character behaviors for continuous games as compact, human-readable programs. Game AI practice still relies heavily on manually author…
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…
MiGumi: Making Tightly Coupled Integral Joints Millable
Aditya Ganeshan, Kurt Fleischer, Wenzel Jakob +4
Traditional integral wood joints, despite their strength, durability, and elegance, remain rare in modern workflows due to the cost and difficulty of manual fabrication. CNC millin…
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…
ProcTex: Consistent and Interactive Text-to-texture Synthesis for Part-based Procedural Models
Ruiqi Xu, Zihan Zhu, Ben Ahlbrand +2
Recent advances in generative modeling have driven significant progress in text-guided texture synthesis. However, current methods focus on synthesizing texture for single static 3…
CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes
Xianghao Xu, Srinath Sridhar, Daniel Ritchie
We propose a zero-shot text-driven 3D shape deformation system that deforms an input 3D mesh of a manufactured object to fit an input text description. To do this, our system optim…
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…
Creating Language-driven Spatial Variations of Icon Images
Xianghao Xu, Aditya Ganeshan, Karl D. D. Willis +2
Editing 2D icon images can require significant manual effort from designers. It involves manipulating multiple geometries while maintaining the logical or physical coherence of the…
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…