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

• URL: http://arxiv.org/abs/2407.16224v1
• Date: Tue, 23 Jul 2024 07:04:42 GMT
• Title: OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person
• Authors: Ke Sun, Jian Cao, Qi Wang, Linrui Tian, Xindi Zhang, Lian Zhuo, Bang Zhang, Liefeng Bo, Wenbo Zhou, Weiming Zhang, Daiheng Gao,
• Abstract summary: OutfitAnyone generates high-fidelity and detail-consistent images for virtual clothing trials. It distinguishes itself with scalability-ulating factors such as pose, body shape and broad applicability. OutfitAnyone's performance in diverse scenarios underscores its utility and readiness for real-world deployment.
• Score: 38.69239957207417
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 \url{https://humanaigc.github.io/outfit-anyone/}.

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