OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any
  Person
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/.
66a0558352e938fff6d1c1e1
Ke Sun
66a0558352e938fff6d1c1e2
Jian Cao
66a0558352e938fff6d1c1e3
Qi Wang
66a0558352e938fff6d1c1e4
Linrui Tian
66a0558352e938fff6d1c1e5
Xindi Zhang
66a0558352e938fff6d1c1e6
Lian Zhuo
admin_assigned
2024-07-24T08:16:09.015Z
6669b27ec5b27e2d625f047b
/avatars/15b76f88854b1ae94bd7bbfe729ef305.svg
Lian Zhuo
user
Hetiey
66a0558352e938fff6d1c1e7
Bang Zhang
66a0558352e938fff6d1c1e8
Liefeng Bo
admin_assigned
2024-07-24T08:16:27.650Z
63d0cc736b985b0f25d0412c
/avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
Bo
user
Liefeng
66a0558352e938fff6d1c1e9
Wenbo Zhou
admin_assigned
2024-07-24T08:16:42.666Z
65855ee8ebe2be208081e69e
https://cdn-avatars.huggingface.co/v1/production/uploads/65855ee8ebe2be208081e69e/CIDPY0H6v4BiLkcxSANxD.jpeg
zhouwenbo
user
bobozi
66a0558352e938fff6d1c1ea
Weiming Zhang
66a0558352e938fff6d1c1eb
Daiheng Gao
OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any
  Person
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/.
OutfitAnyone uses a two-stream conditional diffusion model to achieve high-fidelity garment deformation in virtual try-on scenarios, addressing challenges in control and consistency across diverse images and conditions.
diffusion models Stable Diffusion series conditional generation two-stream conditional diffusion model garment deformation scalability pose body shape in-the-wild images virtual try-on