OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916333149487104 |
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| author | Sun, Ke Cao, Jian Wang, Qi Tian, Linrui Zhang, Xindi Zhuo, Lian Zhang, Bang Bo, Liefeng Zhou, Wenbo Zhang, Weiming Gao, Daiheng |
| author_facet | Sun, Ke Cao, Jian Wang, Qi Tian, Linrui Zhang, Xindi Zhuo, Lian Zhang, Bang Bo, Liefeng Zhou, Wenbo Zhang, Weiming Gao, Daiheng |
| contents | 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/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person Sun, Ke Cao, Jian Wang, Qi Tian, Linrui Zhang, Xindi Zhuo, Lian Zhang, Bang Bo, Liefeng Zhou, Wenbo Zhang, Weiming Gao, Daiheng Computer Vision and Pattern Recognition 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/}. |
| title | OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.16224 |