74 citations · 174 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Aditya Sanghi, Hang Chu, Joseph G. Lambourne +4
Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image genera…
LSD-StructureNet: Modeling Levels of Structural Detail in 3D Part Hierarchies
Dominic Roberts, Ara Danielyan, Hang Chu +2
Generative models for 3D shapes represented by hierarchies of parts can generate realistic and diverse sets of outputs. However, existing models suffer from the key practical limit…
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…
House-GAN++: Generative Adversarial Layout Refinement Networks
Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang +3
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relationa…