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Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials

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arxiv 2407.02445 v1 pith:3O2NAUBS submitted 2024-07-02 cs.CV cs.AIcs.GR

Yawar Siddiqui , Tom Monnier , Filippos Kokkinos , Mahendra Kariya , Yanir Kleiman , Emilien Garreau , Oran Gafni , Natalia Neverova
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Andrea Vedaldi Roman Shapovalov David Novotny
This is my paper · ORCID
classification cs.CVcs.AIcs.GR
keywords assetgentextureappearancebestgenerationhigh-qualitylossmaterials
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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

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment

    cs.CV 2024-12 conditional novelty 7.0 of 10

    HybridMQA combines a 3D graph network with rendered 2D projections via cross-attention to assess colored mesh quality, outperforming prior full-reference methods on four public datasets.

  2. LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

    cs.CV 2026-04 conditional novelty 6.0 of 10

    Scaling transformer context with sparse attention and 3D-aware block routing improves feed-forward 3D reconstruction and inverse rendering, closing much of the quality gap with dense-view optimization.

  3. PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors

    cs.CV 2025-06 conditional novelty 6.0 of 10

    PBR-SR super-resolves PBR texture maps (albedo, roughness, metallic, normal) in a zero-shot way by optimizing textures so differentiable renderings match super-resolved multi-view renderings from a pretrained image SR model.

  4. FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A feed-forward transformer that jointly predicts pixel-aligned 3D Gaussians and camera poses from uncalibrated sparse views.

  5. Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Wavelet Latent Diffusion (WaLa) shrinks 3D shapes to 6,912-variable latent codes and trains billion-parameter diffusion models that generate 256^3 geometry in 2-4 seconds, claiming state-of-the-art results.

  6. ARM: Appearance Reconstruction Model for Relightable 3D Generation

    cs.CV 2024-11 conditional novelty 5.0 of 10

    ARM is a feed-forward model that reconstructs a 3D mesh and PBR texture maps (albedo, roughness, metalness) from sparse-view images, improving texture sharpness and relighting quality over prior single-image-to-3D methods.

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