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.

本研究提出了一种名为Fashion-VDM的视频扩散模型，旨在生成高质量的虚拟试穿视频，同时保持人物的身份和运动。该方法通过扩散架构和逐步时间训练策略解决了现有视频虚拟试穿方法在服装细节和时间一致性上的不足，实验结果表明其在视频虚拟试穿领域达到了新的领先水平。

时尚视频扩散模型：虚拟试穿的视频扩散模型