Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On

• URL: http://arxiv.org/abs/2505.16977v1
• Date: Thu, 22 May 2025 17:52:13 GMT
• Title: Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On
• Authors: Siqi Wan, Jingwen Chen, Yingwei Pan, Ting Yao, Tao Mei,
• Abstract summary: Diffusion models have shown success in virtual try-on (VTON) task.<n>The problem remains challenging to preserve the shape and every detail of the given garment due to the intrinsicity of diffusion model.<n>We propose to explicitly capitalize on visual correspondence as the prior to tame diffusion process.
• Score: 89.9123806553489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have shown preliminary success in virtual try-on (VTON) task. The typical dual-branch architecture comprises two UNets for implicit garment deformation and synthesized image generation respectively, and has emerged as the recipe for VTON task. Nevertheless, the problem remains challenging to preserve the shape and every detail of the given garment due to the intrinsic stochasticity of diffusion model. To alleviate this issue, we novelly propose to explicitly capitalize on visual correspondence as the prior to tame diffusion process instead of simply feeding the whole garment into UNet as the appearance reference. Specifically, we interpret the fine-grained appearance and texture details as a set of structured semantic points, and match the semantic points rooted in garment to the ones over target person through local flow warping. Such 2D points are then augmented into 3D-aware cues with depth/normal map of target person. The correspondence mimics the way of putting clothing on human body and the 3D-aware cues act as semantic point matching to supervise diffusion model training. A point-focused diffusion loss is further devised to fully take the advantage of semantic point matching. Extensive experiments demonstrate strong garment detail preservation of our approach, evidenced by state-of-the-art VTON performances on both VITON-HD and DressCode datasets. Code is publicly available at: https://github.com/HiDream-ai/SPM-Diff.

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