AvatarVTON: 4D Virtual Try-On for Animatable Avatars
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908577984151552 |
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| author | Jiang, Zicheng Gao, Jixin He, Shengfeng Li, Xinzhe Zheng, Yulong Yang, Zhaotong Dong, Junyu Du, Yong |
| author_facet | Jiang, Zicheng Gao, Jixin He, Shengfeng Li, Xinzhe Zheng, Yulong Yang, Zhaotong Dong, Junyu Du, Yong |
| contents | We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view rendering, and diverse garment choices. Unlike existing methods, AvatarVTON supports dynamic garment interactions under single-view supervision, without relying on multi-view garment captures or physics priors. The framework consists of two key modules: (1) a Reciprocal Flow Rectifier, a prior-free optical-flow correction strategy that stabilizes avatar fitting and ensures temporal coherence; and (2) a Non-Linear Deformer, which decomposes Gaussian maps into view-pose-invariant and view-pose-specific components, enabling adaptive, non-linear garment deformations. To establish a benchmark for 4D virtual try-on, we extend existing baselines with unified modules for fair qualitative and quantitative comparisons. Extensive experiments show that AvatarVTON achieves high fidelity, diversity, and dynamic garment realism, making it well-suited for AR/VR, gaming, and digital-human applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AvatarVTON: 4D Virtual Try-On for Animatable Avatars Jiang, Zicheng Gao, Jixin He, Shengfeng Li, Xinzhe Zheng, Yulong Yang, Zhaotong Dong, Junyu Du, Yong Computer Vision and Pattern Recognition We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view rendering, and diverse garment choices. Unlike existing methods, AvatarVTON supports dynamic garment interactions under single-view supervision, without relying on multi-view garment captures or physics priors. The framework consists of two key modules: (1) a Reciprocal Flow Rectifier, a prior-free optical-flow correction strategy that stabilizes avatar fitting and ensures temporal coherence; and (2) a Non-Linear Deformer, which decomposes Gaussian maps into view-pose-invariant and view-pose-specific components, enabling adaptive, non-linear garment deformations. To establish a benchmark for 4D virtual try-on, we extend existing baselines with unified modules for fair qualitative and quantitative comparisons. Extensive experiments show that AvatarVTON achieves high fidelity, diversity, and dynamic garment realism, making it well-suited for AR/VR, gaming, and digital-human applications. |
| title | AvatarVTON: 4D Virtual Try-On for Animatable Avatars |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.04822 |