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

Ben Li; Minqi Li; Jie Ren; Kaibing Zhang

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arXiv · 2505.23439

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

Ben Li·Minqi Li·Jie Ren·Kaibing Zhang

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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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BibTeX RIS

Ben Li, Minqi Li, Jie Ren, Kaibing Zhang. 2025-05-29. VITON-DRR: Details Retention Virtual Try-on via Non-rigid Registration. https://arxiv.org/abs/2505.23439

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