OutfitAnyone：針對任何服裝和任何人的超高品質虛擬試穿

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

虛擬試穿（VTON）已成為一項具有轉型性的技術，使用戶能夠在不必實際試穿服裝的情況下嘗試不同風格。然而，現有方法常常難以生成高保真度和細節一致性的結果。儘管擴散模型，如穩定擴散系列，已展示了它們在創建高質量和照片般逼真圖像方面的能力，但在像VTON這樣的有條件生成場景中，它們遇到了巨大挑戰。具體而言，這些模型在為虛擬試穿生成圖像時往往難以在控制和一致性之間保持平衡。OutfitAnyone通過利用雙流有條件擴散模型來應對服裝變形，從而實現更逼真的結果。它通過姿勢、身體形狀等可調節因素以及廣泛的應用性，從動漫到野外圖像，脫穎而出。OutfitAnyone在各種場景中的表現突顯了其在現實世界部署方面的實用性和準備就緒性。有關更多詳細信息和動畫結果，請參見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/.