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We present DreamPose, a diffusion-based method for generating animatedfashion videos from still images. Given an image and a sequence of human bodyposes, our method synthesizes a video containing both human and fabric motion.To achieve this, we transform a pretrained text-to-image model (StableDiffusion) into a pose-and-image guided video synthesis model, using a novelfine-tuning strategy, a set of architectural changes to support the addedconditioning signals, and techniques to encourage temporal consistency. Wefine-tune on a collection of fashion videos from the UBC Fashion dataset. Weevaluate our method on a variety of clothing styles and poses, and demonstratethat our method produces state-of-the-art results on fashion videoanimation.Video results are available on our project page.

DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion