7 papers
The Norm Spectrum of Barnes-Wall Lattices Below Twice the Minimum
Yuhang Cao, Sihuang Hu, Hexiang Huang +1
Let be the Barnes-Wall lattice of dimension , normalized to have minimum squared norm . We determine its complete norm spectrum strictly between and…
Aggregating LLM-Based Weak Verifiers for Spatial Layout Generation
Sharon Zhang, R. Kenny Jones, Jiajun Wu +1
We present a pipeline for building and aggregating task-specific, LLM-generated weak (imperfect) verifiers into a strong verifier for spatial layout domains. Given a task descripti…
Self-Consistency for LLM-Based Motion Trajectory Generation and Verification
Jiaju Ma, R. Kenny Jones, Jiajun Wu +1
Self-consistency has proven to be an effective technique for improving LLM performance on natural language reasoning tasks in a lightweight, unsupervised manner. In this work, we s…
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…
Learning Body-Aware 3D Shape Generative Models
Bryce Blinn, Alexander Ding, R. Kenny Jones +3
The shape of many objects in the built environment is dictated by their relationships to the human body: how will a person interact with this object? Existing data-driven generativ…
The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference
R. Kenny Jones, Aalia Habib, Rana Hanocka +1
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. NGSP solves this problem via MAP infer…
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
R. Kenny Jones, Homer Walke, Daniel Ritchie
Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more. Training models to perform this task is complicated because paired (shap…