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In this paper, we propose a novel dual-branch Transformation-Synthesisnetwork (TS-Net), for video motion retargeting. Given one subject video and onedriving video, TS-Net can produce a new plausible video with the subjectappearance of the subject video and motion pattern of the driving video. TS-Netconsists of a warp-based transformation branch and a warp-free synthesisbranch. The novel design of dual branches combines the strengths ofdeformation-grid-based transformation and warp-free generation for betteridentity preservation and robustness to occlusion in the synthesized videos. Amask-aware similarity module is further introduced to the transformation branchto reduce computational overhead. Experimental results on face and dancedatasets show that TS-Net achieves better performance in video motionretargeting than several state-of-the-art models as well as its single-branchvariants. Our code is available at https://github.com/nihaomiao/WACV23_TSNet.

Cross-identity Video Motion Retargeting with Joint Transformation and Synthesis