Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908480290422784 |
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| author | Zhang, Angang Deng, Fang Chen, Hao Chen, Zhongjian Li, Junyan |
| author_facet | Zhang, Angang Deng, Fang Chen, Hao Chen, Zhongjian Li, Junyan |
| contents | While recent advances in virtual try-on (VTON) have achieved realistic garment transfer to human subjects, its inverse task, virtual try-off (VTOFF), which aims to reconstruct canonical garment templates from dressed humans, remains critically underexplored and lacks systematic investigation. Existing works predominantly treat them as isolated tasks: VTON focuses on garment dressing while VTOFF addresses garment extraction, thereby neglecting their complementary symmetry. To bridge this fundamental gap, we propose the Two-Way Garment Transfer Model (TWGTM), to the best of our knowledge, the first unified framework for joint clothing-centric image synthesis that simultaneously resolves both mask-guided VTON and mask-free VTOFF through bidirectional feature disentanglement. Specifically, our framework employs dual-conditioned guidance from both latent and pixel spaces of reference images to seamlessly bridge the dual tasks. On the other hand, to resolve the inherent mask dependency asymmetry between mask-guided VTON and mask-free VTOFF, we devise a phased training paradigm that progressively bridges this modality gap. Extensive qualitative and quantitative experiments conducted across the DressCode and VITON-HD datasets validate the efficacy and competitive edge of our proposed approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_04551 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis Zhang, Angang Deng, Fang Chen, Hao Chen, Zhongjian Li, Junyan Computer Vision and Pattern Recognition While recent advances in virtual try-on (VTON) have achieved realistic garment transfer to human subjects, its inverse task, virtual try-off (VTOFF), which aims to reconstruct canonical garment templates from dressed humans, remains critically underexplored and lacks systematic investigation. Existing works predominantly treat them as isolated tasks: VTON focuses on garment dressing while VTOFF addresses garment extraction, thereby neglecting their complementary symmetry. To bridge this fundamental gap, we propose the Two-Way Garment Transfer Model (TWGTM), to the best of our knowledge, the first unified framework for joint clothing-centric image synthesis that simultaneously resolves both mask-guided VTON and mask-free VTOFF through bidirectional feature disentanglement. Specifically, our framework employs dual-conditioned guidance from both latent and pixel spaces of reference images to seamlessly bridge the dual tasks. On the other hand, to resolve the inherent mask dependency asymmetry between mask-guided VTON and mask-free VTOFF, we devise a phased training paradigm that progressively bridges this modality gap. Extensive qualitative and quantitative experiments conducted across the DressCode and VITON-HD datasets validate the efficacy and competitive edge of our proposed approach. |
| title | Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.04551 |