title: Fashion-VDM: Video Diffusion Model for Virtual Try-On
summary: We present Fashion-VDM, a video diffusion model (VDM) for generating virtual
  try-on videos. Given an input garment image and person video, our method aims
  to generate a high-quality try-on video of the person wearing the given
  garment, while preserving the person's identity and motion. Image-based virtual
  try-on has shown impressive results; however, existing video virtual try-on
  (VVT) methods are still lacking garment details and temporal consistency. To
  address these issues, we propose a diffusion-based architecture for video
  virtual try-on, split classifier-free guidance for increased control over the
  conditioning inputs, and a progressive temporal training strategy for
  single-pass 64-frame, 512px video generation. We also demonstrate the
  effectiveness of joint image-video training for video try-on, especially when
  video data is limited. Our qualitative and quantitative experiments show that
  our approach sets the new state-of-the-art for video virtual try-on. For
  additional results, visit our project page:
  https://johannakarras.github.io/Fashion-VDM.
paper_authors._id: 67290abb74956794331a9ede
paper_authors.name: Johanna Karras
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T21:24:13.479Z
paper_authors._id: 63334429518c0f78a6e19d65
paper_authors.avatarUrl: /avatars/5ac96f0308529cb526cc45030b46b550.svg
paper_authors.fullname: Johanna Karras
paper_authors.type: user
paper_authors.user: jskarras
paper_authors._id: 67290abb74956794331a9edf
paper_authors.name: Yingwei Li
paper_authors._id: 67290abb74956794331a9ee0
paper_authors.name: Nan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T07:58:34.153Z
paper_authors._id: 62db5ae193fc0e52427a803a
paper_authors.avatarUrl: /avatars/251e209b60178a2ffc555b2ecdd29cf1.svg
paper_authors.fullname: Nan Liu
paper_authors.type: user
paper_authors.user: nanliu
paper_authors._id: 67290abb74956794331a9ee1
paper_authors.name: Luyang Zhu
paper_authors._id: 67290abb74956794331a9ee2
paper_authors.name: Innfarn Yoo
paper_authors._id: 67290abb74956794331a9ee3
paper_authors.name: Andreas Lugmayr
paper_authors._id: 67290abb74956794331a9ee4
paper_authors.name: Chris Lee
paper_authors._id: 67290abb74956794331a9ee5
paper_authors.name: Ira Kemelmacher-Shlizerman
paper_title: Fashion-VDM: Video Diffusion Model for Virtual Try-On
paper_summary: We present Fashion-VDM, a video diffusion model (VDM) for generating virtual
  try-on videos. Given an input garment image and person video, our method aims
  to generate a high-quality try-on video of the person wearing the given
  garment, while preserving the person's identity and motion. Image-based virtual
  try-on has shown impressive results; however, existing video virtual try-on
  (VVT) methods are still lacking garment details and temporal consistency. To
  address these issues, we propose a diffusion-based architecture for video
  virtual try-on, split classifier-free guidance for increased control over the
  conditioning inputs, and a progressive temporal training strategy for
  single-pass 64-frame, 512px video generation. We also demonstrate the
  effectiveness of joint image-video training for video try-on, especially when
  video data is limited. Our qualitative and quantitative experiments show that
  our approach sets the new state-of-the-art for video virtual try-on. For
  additional results, visit our project page:
  https://johannakarras.github.io/Fashion-VDM.
paper_ai_keywords: video diffusion model (VDM) try-on videos diffusion-based video virtual try-on (VVT) split classifier-free conditioning inputs progressive temporal training strategy single-pass 64-frame, 512px video generation joint-image-video training video try-on