VITON-DRR: Details Retention Virtual Try-on via Non-rigid Registration

• URL: http://arxiv.org/abs/2505.23439v1
• Date: Thu, 29 May 2025 13:38:21 GMT
• Title: VITON-DRR: Details Retention Virtual Try-on via Non-rigid Registration
• Authors: Ben Li, Minqi Li, Jie Ren, Kaibing Zhang,
• Abstract summary: This paper proposes a detail retention virtual try-on method via accurate non-rigid registration (VITON-DRR) for diverse human poses.<n> Specifically, we reconstruct a human semantic segmentation using a dual-pyramid-structured feature extractor.<n>Then, a novel Deformation Module is designed for extracting the cloth key points and warping them through an accurate non-rigid registration algorithm.
• Score: 5.465426769865638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-based virtual try-on aims to fit a target garment to a specific person image and has attracted extensive research attention because of its huge application potential in the e-commerce and fashion industries. To generate high-quality try-on results, accurately warping the clothing item to fit the human body plays a significant role, as slight misalignment may lead to unrealistic artifacts in the fitting image. Most existing methods warp the clothing by feature matching and thin-plate spline (TPS). However, it often fails to preserve clothing details due to self-occlusion, severe misalignment between poses, etc. To address these challenges, this paper proposes a detail retention virtual try-on method via accurate non-rigid registration (VITON-DRR) for diverse human poses. Specifically, we reconstruct a human semantic segmentation using a dual-pyramid-structured feature extractor. Then, a novel Deformation Module is designed for extracting the cloth key points and warping them through an accurate non-rigid registration algorithm. Finally, the Image Synthesis Module is designed to synthesize the deformed garment image and generate the human pose information adaptively. {Compared with} traditional methods, the proposed VITON-DRR can make the deformation of fitting images more accurate and retain more garment details. The experimental results demonstrate that the proposed method performs better than state-of-the-art methods.

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