FlexPainter：灵活且多视角一致的纹理生成技术

FlexPainter: Flexible and Multi-View Consistent Texture Generation

June 3, 2025

作者: Dongyu Yan, Leyi Wu, Jiantao Lin, Luozhou Wang, Tianshuo Xu, Zhifei Chen, Zhen Yang, Lie Xu, Shunsi Zhang, Yingcong Chen

cs.AI

摘要

纹理贴图制作是三维建模中的重要环节，直接决定了渲染质量。近年来，基于扩散模型的方法为纹理生成开辟了新途径。然而，受限的控制灵活性和有限的提示模态可能阻碍创作者实现预期效果。此外，生成的多视角图像间的不一致性往往导致纹理生成质量欠佳。针对这些问题，我们提出了FlexPainter，一种新颖的纹理生成流程，它支持灵活的多模态条件引导，并实现了高度一致的纹理生成。我们构建了一个共享的条件嵌入空间，以在不同输入模态间进行灵活聚合。利用这一嵌入空间，我们提出了一种基于图像的CFG方法，用于分解结构与风格信息，实现基于参考图像的风格化。借助图像扩散先验中的三维知识，我们首先采用网格表示同时生成多视角图像，以增强全局理解。同时，我们在扩散采样过程中引入了视角同步与自适应加权模块，进一步确保局部一致性。最后，结合纹理增强模型的三维感知纹理补全模型被用于生成无缝、高分辨率的纹理贴图。综合实验表明，我们的框架在灵活性和生成质量上均显著优于现有最先进方法。

English

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 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.