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In this paper, we propose a deep learning framework for unsupervised motion retargeting. In contrast to the existing method, we decouple the motion retargeting process into two parts that explicitly learn poses and movements of a character. Here, the first part retargets the pose of the character at each frame, while the second part retargets the character's overall movement. To realize these two processes, we develop a novel architecture referred to as the pose-movement network (PMnet), whichdblp:conf/bmvc/LimCC19 fatcat:e5qgxywyinhlna3cx7zj73srta