FlexPainter: Flexible and Multi-View Consistent Texture Generation

• URL: http://arxiv.org/abs/2506.02620v1
• Date: Tue, 03 Jun 2025 08:36:03 GMT
• Title: FlexPainter: Flexible and Multi-View Consistent Texture Generation
• Authors: Dongyu Yan, Leyi Wu, Jiantao Lin, Luozhou Wang, Tianshuo Xu, Zhifei Chen, Zhen Yang, Lie Xu, Shunsi Zhang, Yingcong Chen,
• Abstract summary: textbfFlexPainter is a novel texture generation pipeline that enables flexible multi-modal conditional guidance.<n>Our framework significantly outperforms state-of-the-art methods in both flexibility and generation quality.
• Score: 15.727635740684157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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