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This paper introduces Unified Motion Retargeting (UMR), a novel framework that learns dense point cloud correspondence to facilitate the retargeting of human motion to humanoid robots without relying on manually designed mappings. By utilizing exterior point clouds as a unified interface, UMR effectively decouples the retargeting process from specific skeletal semantics and robot topologies, allowing for improved pose alignment and interaction transfer. Experimental results show that UMR outperforms existing methods in motion fidelity and plausibility across diverse motion sources and robot designs, establishing a scalable approach for generating robot-ready training data from human motion references.
UMR achieves unprecedented motion fidelity by learning dense point cloud correspondences, eliminating the need for manual semantic mappings in humanoid motion retargeting.
Humanoid learning increasingly relies on transforming vast and diverse human motion data into high-quality robot reference trajectories. However, retargeting human motion to humanoid robots is challenging due to substantial differences in morphology, degrees of freedom, joint ranges, and kinematic constraints between humans and robots. Existing retargeting methods typically address these differences by defining human-robot correspondence through hand-crafted sparse keypoints or body-part pairs. As a result, retargeting quality depends heavily on manual semantic design, limiting scalability across motion sources and robot morphologies and providing only sparse guidance for reproducing detailed poses and interactions. In this paper, we present Unified Motion Retargeting (UMR), a framework that learns dense point cloud correspondence without requiring manually designed human-robot mappings. By treating exterior point clouds as a unified interface between human motion and humanoid robots, UMR decouples retargeting from source-specific skeletal semantics and robot-specific topology. The learned dense correspondence provides fine-grained geometric anchors for constrained point cloud matching optimization, enabling surface-level pose alignment and direct transfer of interaction contacts. Experiments demonstrate that UMR unifies retargeting across heterogeneous motion sources, robot embodiments, and downstream scenarios ranging from locomotion to interaction, while achieving higher motion fidelity and plausibility than state-of-the-art methods. UMR therefore provides a scalable foundation for transforming large-scale human motion references into robot-ready training data.
