April 25, 2024, 7:46 p.m. | Lior Yariv, Omri Puny, Natalia Neverova, Oran Gafni, Yaron Lipman

cs.CV updates on arXiv.org arxiv.org

arXiv:2312.09222v2 Announce Type: replace
Abstract: Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a diffusion or flow models on 3D shapes a crucial design choice is the shape representation. An effective shape representation needs to adhere three design principles: it should allow an efficient conversion of large 3D datasets to the representation form; it should provide a good tradeoff of approximation power …

2d image abstract arxiv cs.cv cs.gr current design diffusion diffusion models flow generative generative models image image diffusion mosaic representation training type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne