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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.
Adapting motion priors to specific task contexts can dramatically enhance humanoid control performance and efficiency, outperforming traditional uniform approaches.
A new flow matching prior can drastically enhance humanoid motion tracking by guiding policy exploration with geometric insights from unordered pose data.
