AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912697929433088 |
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| author | Qiu, Yuda Xiao, Zitong Zuo, Yiwei Ye, Zisheng Chen, Weikai Han, Xiaoguang |
| author_facet | Qiu, Yuda Xiao, Zitong Zuo, Yiwei Ye, Zisheng Chen, Weikai Han, Xiaoguang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06721 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars Qiu, Yuda Xiao, Zitong Zuo, Yiwei Ye, Zisheng Chen, Weikai Han, Xiaoguang Computer Vision and Pattern Recognition 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. |
| title | AvatarTex: High-Fidelity Facial Texture Reconstruction from Single-Image Stylized Avatars |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.06721 |