关键词: 虚拟试衣, 细节增强, 坐标注意力机制, 以人为中心, 扩散模型

Abstract: To address the limitations of current virtual try-on methods in adequately preserving local details of target garments, and when diffusion model were used for generation, the Variational AutoEncoder (VAE)’s mapping of input data to low-dimensional space leaded to loss of high-frequency details in models’ hands and faces, a human-centric detail-enhanced virtual try-on method was proposed. Firstly, the clothing-agnostic person map, pose map, and target garment were input into a geometric matching module to generate a coarsely warped garment. Then, a Garment Wrap Refinement (GWR) was constructed to enhance the detailed features of the coarsely warped garment. Subsequently, the warped garment, clothing-agnostic person map, and pose map were concatenated and fed into a UNet alongside textual features, where textual and visual features were fused to progressively generate a clear image through iterative denoising. A Mask Feature Connection (MFC) moduleincorporating a coordinate attention was introduced to precisely localize the model’s position and preserve high-frequency details in regions such as hands and faces, ensuring human-centric results. Finally, the outputs of the (MFC) module and the UNet were fused and decoded to produce the final try-on result. Experimental results demonstrate that the proposed method achieves a 1.41% improvement in the Structural Similarity Index Measure (SSIM) on the DressCode dataset, along with reductions of 7.32%, 31%, and 65% in the Learned Perceptual Image Patch Similarity (LPIPS), Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) metrics, respectively, compared to the LADI-VTON method, these results indicate that the proposed method achieves superior performance in virtual try-on tasks.

Key words: virtual try-on, detail enhancement, coordinate attention, human centric, diffusion model