@inproceedings{
siddiqui2024meta,
title={Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and {PBR} Materials},
author={Yawar Siddiqui and Tom Monnier and Filippos Kokkinos and Mahendra Kariya and Yanir Kleiman and Emilien Garreau and Oran Gafni and Natalia Neverova and Andrea Vedaldi and Roman Shapovalov and David Novotny},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=M3BIsgGQNb}
}
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 a
corresponding 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
Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
Yawar Siddiqui Tom Monnier Filippos Kokkinos Mahendra Kariya Yanir Kleiman Emilien Garreau Oran Gafni Natalia Neverova Andrea Vedaldi Roman Shapovalov David Novotny