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In this work, we propose MagicPose, a diffusion-based model for 2D human poseand facial expression retargeting. Specifically, given a reference image, weaim to generate a person's new images by controlling the poses and facialexpressions while keeping the identity unchanged. To this end, we propose atwo-stage training strategy to disentangle human motions and appearance (e.g.,facial expressions, skin tone and dressing), consisting of (1) the pre-trainingof an appearance-control block and (2) learning appearance-disentangled posecontrol. Our novel design enables robust appearance control over generatedhuman images, including body, facial attributes, and even background. Byleveraging the prior knowledge of image diffusion models, MagicPose generalizeswell to unseen human identities and complex poses without the need foradditional fine-tuning. Moreover, the proposed model is easy to use and can beconsidered as a plug-in module/extension to Stable Diffusion. The code isavailable at: https://github.com/Boese0601/MagicDance

MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion