Fashion-VDM: Video Diffusion Model for Virtual Try-On
Authors: name: Johanna Karras, name: Yingwei Li, name: Nan Liu, name: Luyang Zhu, name: Innfarn Yoo, name: Andreas Lugmayr, name: Chris Lee, name: Ira Kemelmacher-Shlizerman

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.