MIRRORED-Anims: Motion Inversion for Rig-space Retargeting to Obtain a Reliable Enlarged Dataset of Character Animations

We propose MIRRORED-Anims, a novel retargeting procedure for transferring motion between skinned humanoid characters of different morphologies. It is designed so as to mimic the strengths of the closed-source Mixamo’s retargeting method, currently used as a standard to create motion databases and train all state-of-the-art learning-based retargeting methods, despite severe shortcomings (namely, a lack of character diversity and notable penetration artifacts). Taking inspiration from the toolsets of 3D animators, our retargeting algorithm relies on the control rigs used to manipulate skinned characters, by identifying and transferring controller values on predefined bone mechanisms. While producing motions which are closer to Mixamo’s ground truth than any state-of-the-art learning-based technique, MIRRORED-Anims creates fewer penetration artifacts than observed in the Mixamo dataset, improving the perceived quality of the output. Moreover, motion can be retargeted in real-time to and from the SMPL body model, making it possible to leverage the large motion databases available in SMPL format for the retargeting task. Because it relies solely on transparent, explainable rig operations, MIRRORED-Anims can be used to generate ground-truth motions for any humanoid character, providing a reliable baseline for the future training of learning-based methods. Project page: https://mirrored-anims.github.io/MIRRORED-Anims