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.

翻译：本文提出Fashion-VDM，一种用于生成虚拟试穿视频的视频扩散模型。给定输入的服装图像和人物视频，我们的方法旨在生成人物穿着指定服装的高质量试穿视频，同时保持人物身份特征与运动姿态。基于图像的虚拟试穿已展现出令人印象深刻的效果；然而，现有的视频虚拟试穿方法仍存在服装细节缺失与时序一致性问题。为解决这些挑战，我们提出一种基于扩散架构的视频虚拟试穿方案，包括：采用分割分类器自由引导以增强对条件输入的控制，以及通过渐进式时序训练策略实现单次生成64帧、512像素的视频。我们还验证了在视频数据有限时，联合图像-视频训练对视频试穿任务的有效性。定性与定量实验表明，我们的方法在视频虚拟试穿任务上达到了新的最优性能。更多结果请访问项目页面：https://johannakarras.github.io/Fashion-VDM。