Kim, SangBinPark, InbumKwon, SeongsuHan, JungHyunPanozzo, Daniele and Assarsson, Ulf2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13947https://diglib.eg.org:443/handle/10.1111/cgf13947Motion retargetting refers to the process of adapting the motion of a source character to a target. This paper presents a motion retargetting model based on temporal dilated convolutions. In an unsupervised manner, the model generates realistic motions for various humanoid characters. The retargetted motions not only preserve the high-frequency detail of the input motions but also produce natural and stable trajectories despite the skeleton size differences between the source and target. Extensive experiments are made using a 3D character motion dataset and a motion capture dataset. Both qualitative and quantitative comparisons against prior methods demonstrate the effectiveness and robustness of our method.Attribution 4.0 International LicenseComputing methodologiesNeural networks10.1111/cgf.13947497-507