AvatarTex：从单张图像风格化头像进行高保真面部纹理重建
AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars
AI总结：
AvatarTex提出三阶段扩散到GAN流水线，结合扩散模型的纹理多样性与GAN的UV拓扑一致性，并引入含2万张多风格UV纹理的TexHub数据集，实现从单张图像进行高保真风格化面部纹理重建。
摘要：
我们提出AvatarTex，一个高保真面部纹理重建框架，能够从单张图像生成风格化和照片级真实感纹理。现有方法在处理风格化头像时面临困难，原因在于缺乏多样化的多风格数据集，以及在非标准纹理中保持几何一致性方面存在挑战。为解决这些局限，AvatarTex引入了一种新颖的三阶段扩散到GAN流水线。我们的核心见解是：扩散模型擅长生成多样化纹理，但缺乏显式的UV约束；而GAN提供了结构良好的隐空间，能够确保风格和拓扑一致性。通过整合这些优势，AvatarTex实现了高质量、拓扑对齐的纹理合成，兼具艺术性与几何一致性。具体而言，我们的三阶段流水线首先通过基于扩散的修复补全缺失纹理区域，然后利用基于GAN的隐空间优化细化风格和结构一致性，最后通过基于扩散的重绘增强细节。为满足风格化纹理数据集的需求，我们引入了TexHub，这是一个包含20,000个多风格UV纹理的高分辨率集合，具有精确的UV对齐布局。通过利用TexHub和我们的结构化扩散到GAN流水线，AvatarTex在多风格面部纹理重建方面建立了新的最先进水平。TexHub将在发表后发布，以促进该领域的未来研究。
英文摘要：
We present AvatarTex, a high-fidelity facial texture reconstruction framework capable of generating both stylized and photorealistic textures from a single image. Existing methods struggle with stylized avatars due to the lack of diverse multi-style datasets and challenges in maintaining geometric consistency in non-standard textures. To address these limitations, AvatarTex introduces a novel three-stage diffusion-to-GAN pipeline. Our key insight is that while diffusion models excel at generating diversified textures, they lack explicit UV constraints, whereas GANs provide a well-structured latent space that ensures style and topology consistency. By integrating these strengths, AvatarTex achieves high-quality topology-aligned texture synthesis with both artistic and geometric coherence. Specifically, our three-stage pipeline first completes missing texture regions via diffusion-based inpainting, refines style and structure consistency using GAN-based latent optimization, and enhances fine details through diffusion-based repainting. To address the need for a stylized texture dataset, we introduce TexHub, a high-resolution collection of 20,000 multi-style UV textures with precise UV-aligned layouts. By leveraging TexHub and our structured diffusion-to-GAN pipeline, AvatarTex establishes a new state-of-the-art in multi-style facial texture reconstruction. TexHub will be released upon publication to facilitate future research in this field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
