July 02, 2024
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3D shapes and textures in under a minute. It supports physically-based rendering (PBR), necessary for 3D asset relighting in real-world applications. Additionally, 3DGen supports generative retexturing of previously generated (or artist-created) 3D shapes using additional textual inputs provided by the user. 3DGen integrates key technical components, Meta 3D AssetGen and Meta 3D TextureGen, that we developed for text-to-3D and text-to-texture generation, respectively. By combining their strengths, 3DGen represents 3D objects simultaneously in three ways: in view space, in volumetric space, and in UV (or texture) space. The integration of these two techniques achieves a win rate of 68% with respect to the single-stage model. We compare 3DGen to numerous industry baselines, and show that it outperforms them in terms of prompt fidelity and visual quality for complex textual prompts, while being significantly faster.
Written by
Raphael Bensadoun
Tom Monnier
Yanir Kleiman
Yawar Siddiqui
Mahendra Kariya
Omri Harosh
Roman Shapovalov
Emilien Garreau
Animesh Karnewar
Ang Cao
Idan Azuri
Iurii Makarov
Eric-Tuan Le
Antoine Toisoul
David Novotny
Andrea Vedaldi
Publisher
Arxiv only
July 02, 2024
Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotny
July 02, 2024
July 02, 2024
Raphael Bensadoun, Yanir Kleiman, Idan Azuri, Omri Harosh, Andrea Vedaldi, Natalia Neverova, Oran Gafni
July 02, 2024
June 20, 2024
Weiyao Wang, Pierre Gleize, Hao Tang, Xingyu Chen, Kevin Liang, Matt Feiszli
June 20, 2024
June 17, 2024
Jiawei Ren, Frost Xu, Jerry Wu, Ziwei Liu, Tao Xiang, Antoine Toisoul
June 17, 2024
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