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 Novotný
摘要
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 factored 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
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper7
- Hi3dgen: High-Fidelity 3D Geometry Generation From Images Via Normal BridgingChongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang 等ICCV 2025 · 被引用 11 次
- FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D ReconstructionJiale Xu, Shenghua Gao, Ying ShanICCV 2025 · 被引用 8 次
- PBR-SR: Mesh PBR Texture Super Resolution from 2D Image PriorsYujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl 等NeurIPS 2025 · 被引用 6 次
- AnchorDS: Anchoring Dynamic Sources for Semantically Consistent Text-to-3D GenerationJiayin Zhu, Linlin Yang, Yicong Li, Angela YaoAAAI 2026 · 被引用 2 次
- JRM: Joint Reconstruction Model for Multiple Objects without AlignmentQirui Wu, Mohd Yawar Nihal Siddiqui, Duncan Frost, Samir Aroudj 等CVPR 2026 · 被引用 2 次
它引用的顶会 Paper56
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 被引用 4,089 次
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt 等NeurIPS 2021 · 被引用 2,500 次
- Zero-1-to-3: Zero-shot One Image to 3D ObjectRuoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov 等ICCV 2023 · 被引用 1,662 次
- ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score DistillationZhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao 等NeurIPS 2023 · 被引用 1,498 次
相关 Paper
- MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data AugmentationZilong Chen, Yikai Wang, Wenqiang Sun, Feng Wang 等CVPR 2025
- PBR3DGen: A VLM-Guided Mesh Generation with High-Quality PBR TextureXiaokang Wei, Bowen Zhang, Xianghui Yang, Yuxuan Wang 等AAAI 2026 · 被引用 1 次
- MaterialMVP: Illumination-Invariant Material Generation via Multi-View PBR DiffusionZebin He, Mingxin Yang, Shuhui Yang, Yixuan Tang 等ICCV 2025 · 被引用 3 次
- PartGen: Part-level 3D Generation and Reconstruction with Multi-view Diffusion ModelsMinghao Chen, Roman Shapovalov, Iro Laina, Tom Monnier 等CVPR 2025
- Deep Inverse Shading: Consistent Albedo and Surface Detail Recovery via Generative RefinementJiacheng Wu, Ruiqi Zhang, Jie ChenAAAI 2026
