Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis

• URL: http://arxiv.org/abs/2508.04551v1
• Date: Wed, 06 Aug 2025 15:37:16 GMT
• Title: Two-Way Garment Transfer: Unified Diffusion Framework for Dressing and Undressing Synthesis
• Authors: Angang Zhang, Fang Deng, Hao Chen, Zhongjian Chen, Junyan Li,
• Abstract summary: We propose a framework for joint clothing-centric image synthesis that simultaneously resolves mask-guided VTON and mask-free VTOFF.<n>Specifically, our framework employs dual-conditioned guidance from both latent and pixel spaces of reference images to seamlessly bridge the dual tasks.<n>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.
• Score: 5.716907666817588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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