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Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects

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arxiv 2407.02430 v1 pith:V537DS36 submitted 2024-07-02 cs.CV cs.AIcs.GRcs.LG

classification cs.CVcs.AIcs.GRcs.LG
keywords texturegenerationtext-to-imagearbitraryconsistentfasthigh-qualityintroduce
verification ladder T0 review T1 audit T2 compute T3 formal
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The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects. Although recent texture generation methods achieve impressive results by using text-to-image networks, the combination of global consistency, quality, and speed, which is crucial for advancing texture generation to real-world applications, remains elusive. To that end, we introduce Meta 3D TextureGen: a new feedforward method comprised of two sequential networks aimed at generating high-quality and globally consistent textures for arbitrary geometries of any complexity degree in less than 20 seconds. Our method achieves state-of-the-art results in quality and speed by conditioning a text-to-image model on 3D semantics in 2D space and fusing them into a complete and high-resolution UV texture map, as demonstrated by extensive qualitative and quantitative evaluations. In addition, we introduce a texture enhancement network that is capable of up-scaling any texture by an arbitrary ratio, producing 4k pixel resolution textures.

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Cited by 4 Pith papers

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

  1. SeqTex: Generate Mesh Textures in Video Sequence

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SeqTex adapts a pretrained video diffusion model to directly generate complete UV texture maps by jointly predicting four multi-view images and the UV map as a five-frame sequence.

  2. Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A video-diffusion pipeline conditioned on geometry maps, followed by component-wise UV inpainting, produces more coherent and seam-free textures for 3D meshes than Text2Tex, Paint3D, and Meshy in the reported tests.

  3. FlexPainter: Flexible and Multi-View Consistent Texture Generation

    cs.GR 2025-06 conditional novelty 6.0 of 10

    FlexPainter combines multi-view grid generation, UV-space view synchronization with a learned weighting network, and multi-modal embedding control to generate consistent, high-resolution textures from text and image prompts.

  4. UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

    cs.CV 2025-05 conditional novelty 5.0 of 10

    UniTEX generates textures for 3D shapes by predicting continuous volumetric texture functions, bypassing UV maps.

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