Meta 3D AssetGen: Text-to-mesh generation with high-quality geometry, texture, and PBR materials

Conference item

We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared to works that bake shading in the 3D object’s appearance, AssetGen outputs physically-based rendering (PBR) materials, supporting realistic relighting. AssetGen generates first several views of the object with separate shaded and albedo appearance channels, and then reconstructs colours, metalness and roughness in 3D, using a deferred shading loss for efficient supervision. It also uses a sign-distance function to represent 3D shape more reliably and introduces acorresponding loss for direct shape supervision. This is implemented using fused kernels for high memory efficiency. After mesh extraction, a texture refinement transformer operating in UV space significantly improves sharpness and details. AssetGen achieves 17% improvement in Chamfer Distance and 40% in LPIPS over the best concurrent work for few-view reconstruction, and a human preference of 72% over the best industry competitors of comparable speed, including those that support PBR. Project page with generated assets: https://assetgen.github.io

Authors: Siddiqui, Y; Monnier, T; Kokkinos, F; Kariya, M; Kleiman, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Curran Associates
• Host title: Advances in Neural Information Processing Systems 37: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)
• Pages: 9532-9564
• Series: Advances in Neural Information Processing Systems
• Series number: 37
• Publication date: 2024-12-16
• Acceptance date: 2024-09-25
• Event title: 38th Conference on Neural Information Processing Systems (NeurIPS 2024)
• Event location: Vancouver Convention Centre, Vancouver, BC, Canada
• Event website: https://neurips.cc/virtual/2024/index.html
• Event start date: 2023-12-09
• Event end date: 2024-12-15
• ISSN: 1049-5258
• ISBN: 9798331314385
• Language: English
• Keywords: text to 3d; 3d generative models; sparse view reconstruction; 3d shape generation