Virtual clothes try-on has emerged as a vital feature in online shopping,
offering consumers a critical tool to visualize how clothing fits. In our
research, we introduce an innovative approach for virtual clothes try-on,
utilizing a self-supervised Vision Transformer (ViT) coupled with a diffusion
model. Our method emphasizes detail enhancement by contrasting local clothing
image embeddings, generated by ViT, with their global counterparts. Techniques
such as conditional guidance and focus on key regions have been integrated into
our approach. These combined strategies empower the diffusion model to
reproduce clothing details with increased clarity and realism. The experimental
results showcase substantial advancements in the realism and precision of
details in virtual try-on experiences, significantly surpassing the
capabilities of existing technologies.

虚拟试衣技术的创新方法，结合自监督视觉变换器和扩散模型，通过对比局部衣物图像嵌入与全局嵌入来实现细节增强，条件引导和关注关键区域等技术的融合进一步提高了虚拟试衣体验的真实感和精确度。