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UMR achieves unprecedented motion fidelity by learning dense point cloud correspondences, eliminating the need for manual semantic mappings in humanoid motion retargeting.
A new flow matching prior can drastically enhance humanoid motion tracking by guiding policy exploration with geometric insights from unordered pose data.
