69 citations · 173 across the 15 of their papers we have counts for
16 papers
BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry
Xiang Xu, Joseph G. Lambourne, Pradeep Kumar Jayaraman +3
This paper presents BrepGen, a diffusion-based generative approach that directly outputs a Boundary representation (B-rep) Computer-Aided Design (CAD) model. BrepGen represents a B…
Hierarchical Neural Coding for Controllable CAD Model Generation
Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne +2
This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neur…
Next Steps for Human-Centered Generative AI: A Technical Perspective
Xiang 'Anthony' Chen, Jeff Burke, Ruofei Du +8
Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays…
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…
Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly
Yunsheng Tian, Jie Xu, Yichen Li +5
Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, p…
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…
Reconstructing editable prismatic CAD from rounded voxel models
Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman +3
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications. In this work, we introduce a novel neural net…
Mates2Motion: Learning How Mechanical CAD Assemblies Work
James Noeckel, Benjamin T. Jones, Karl Willis +2
We describe our work on inferring the degrees of freedom between mated parts in mechanical assemblies using deep learning on CAD representations. We train our model using a large d…
SimCURL: Simple Contrastive User Representation Learning from Command Sequences
Hang Chu, Amir Hosein Khasahmadi, Karl D. D. Willis +5
User modeling is crucial to understanding user behavior and essential for improving user experience and personalized recommendations. When users interact with software, vast amount…
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Xiang Xu, Karl D. D. Willis, Joseph G. Lambourne +3
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations. Our model util…
SolidGen: An Autoregressive Model for Direct B-rep Synthesis
Pradeep Kumar Jayaraman, Joseph G. Lambourne, Nishkrit Desai +3
The Boundary representation (B-rep) format is the de-facto shape representation in computer-aided design (CAD) to model solid and sheet objects. Recent approaches to generating CAD…
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu +8
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by alignin…
Inferring CAD Modeling Sequences Using Zone Graphs
Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng +2
In computer-aided design (CAD), the ability to "reverse engineer" the modeling steps used to create 3D shapes is a long-sought-after goal. This process can be decomposed into two s…
Engineering Sketch Generation for Computer-Aided Design
Karl D. D. Willis, Pradeep Kumar Jayaraman, Joseph G. Lambourne +2
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects. In this paper we tackle the problem of le…
BRepNet: A topological message passing system for solid models
Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman +3
Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications. They combine lightweight parametric curves and surf…
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
Karl D. D. Willis, Yewen Pu, Jieliang Luo +5
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despi…