OutfitAnyone：任何服装和任何人的超高质量虚拟试穿

OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person

July 23, 2024

作者: Ke Sun, Jian Cao, Qi Wang, Linrui Tian, Xindi Zhang, Lian Zhuo, Bang Zhang, Liefeng Bo, Wenbo Zhou, Weiming Zhang, Daiheng Gao

cs.AI

摘要

虚拟试穿（VTON）已成为一项变革性技术，赋予用户在无需实际试穿服装的情况下尝试时尚的能力。然而，现有方法常常难以生成高保真度和细节一致的结果。扩散模型，如稳定扩散系列，展示了它们在创建高质量和逼真图像方面的能力，但在诸如VTON之类的条件生成场景中遇到了巨大挑战。具体而言，这些模型在为虚拟试穿生成图像时往往难以在控制和一致性之间保持平衡。OutfitAnyone通过利用双流条件扩散模型来解决这些限制，使其能够熟练处理服装变形，从而获得更逼真的结果。它通过姿势、体型等可扩展调节因素以及广泛适用性区分自己，适用范围从动漫到野外图像。OutfitAnyone在多样化场景中的表现凸显了其实用性和可部署性。有关更多详细信息和动画结果，请访问https://humanaigc.github.io/outfit-anyone/。

English

Virtual Try-On (VTON) has become a transformative technology, empowering users to experiment with fashion without ever having to physically try on clothing. However, existing methods often struggle with generating high-fidelity and detail-consistent results. While diffusion models, such as Stable Diffusion series, have shown their capability in creating high-quality and photorealistic images, they encounter formidable challenges in conditional generation scenarios like VTON. Specifically, these models struggle to maintain a balance between control and consistency when generating images for virtual clothing trials. OutfitAnyone addresses these limitations by leveraging a two-stream conditional diffusion model, enabling it to adeptly handle garment deformation for more lifelike results. It distinguishes itself with scalability-modulating factors such as pose, body shape and broad applicability, extending from anime to in-the-wild images. OutfitAnyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment. For more details and animated results, please see https://humanaigc.github.io/outfit-anyone/.