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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T145600
DTEND;TZID=Asia/Tokyo:20241205T150800
UID:siggraphasia_SIGGRAPH Asia 2024_sess134_papers_561@linklings.com
SUMMARY:Fashion-VDM: Video Diffusion Model for Virtual Try-On
DESCRIPTION:Technical Papers\n\nJohanna Karras (Google Research, Universit
 y of Washington); Yingwei Li and Nan Liu (Google Research); Luyang Zhu (Go
 ogle Research, University of Washington); Innfarn Yoo, Andreas Lugmayr, an
 d Chris Lee (Google Research); and Ira Kemelmacher-Shlizerman (Google Rese
 arch, University of Washington)\n\nWe present Fashion-VDM, a video diffusi
 on model (VDM) for generating virtual try-on videos. Given an input garmen
 t image and person video, our method aims to generate a high-quality try-o
 n video of the person wearing the given garment, while preserving the pers
 on's identity and motion. Image-based virtual try-on has shown impressive 
 results; however, existing video virtual try-on (VVT) methods are still la
 cking garment details and temporal consistency. To address these issues, w
 e propose a diffusion-based architecture for video virtual try-on, split c
 lassifier-free guidance for increased control over the conditioning inputs
 , and a progressive temporal training strategy for single-pass 64-frame, 5
 12px video generation. We also demonstrate the effectiveness of joint imag
 e-video training for video try-on, especially when video data is limited. 
 Our qualitative and quantitative experiments show that our approach sets t
 he new state-of-the-art for video virtual try-on.\n\nRegistration Category
 : Full Access, Full Access Supporter\n\nLanguage Format: English Language\
 n\nSession Chair: Nanxuan Zhao (Adobe Research)
URL:https://asia.siggraph.org/2024/program/?id=papers_561&sess=sess134
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