Fashion-VDM: Video Diffusion Model for Virtual Try-On
| Autor: | Karras, Johanna, Li, Yingwei, Liu, Nan, Zhu, Luyang, Yoo, Innfarn, Lugmayr, Andreas, Lee, Chris, Kemelmacher-Shlizerman, Ira |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted to SIGGRAPH Asia 2024 |
| Databáze: | arXiv |
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