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FlexPainter: Flexible and Multi-View Consistent Texture Generation

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arxiv 2506.02620 v1 pith:57VDA4CH submitted 2025-06-03 cs.GR cs.CV

FlexPainter: Flexible and Multi-View Consistent Texture Generation

classification cs.GR cs.CV
keywords texturegenerationflexiblemulti-viewqualityconditionalconsistentdiffusion
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. However, restricted control flexibility and limited prompt modalities may prevent creators from producing desired results. Furthermore, inconsistencies between generated multi-view images often lead to poor texture generation quality. To address these issues, we introduce \textbf{FlexPainter}, a novel texture generation pipeline that enables flexible multi-modal conditional guidance and achieves highly consistent texture generation. A shared conditional embedding space is constructed to perform flexible aggregation between different input modalities. Utilizing such embedding space, we present an image-based CFG method to decompose structural and style information, achieving reference image-based stylization. Leveraging the 3D knowledge within the image diffusion prior, we first generate multi-view images simultaneously using a grid representation to enhance global understanding. Meanwhile, we propose a view synchronization and adaptive weighting module during diffusion sampling to further ensure local consistency. Finally, a 3D-aware texture completion model combined with a texture enhancement model is used to generate seamless, high-resolution texture maps. Comprehensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods in both flexibility and generation quality.

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Cited by 3 Pith papers

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

  1. Variational Test-time Optimization for Diffusion Synchronization

    cs.CV 2026-06 unverdicted novelty 6.0

    Derives an optimal control-based variational optimization framework for test-time diffusion synchronization to enhance collaborative generation across modalities.

  2. Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation

    cs.CV 2026-04 unverdicted novelty 5.0

    Hitem3D 2.0 combines multi-view image synthesis with native 3D texture projection to improve completeness, cross-view consistency, and geometry alignment over prior methods.

  3. Accelerated Likelihood Maximization for Diffusion-based Versatile Content Generation

    cs.CV 2026-06 unverdicted novelty 4.0

    ALM integrates likelihood maximization and acceleration into diffusion reverse sampling to enable globally coherent generation from incomplete inputs.