HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

Humanoid motion tracking is central to teleoperation andwhole-body imitation, yet evaluation often disagrees with what peo-ple perceive in videos. Kinematic errors average per-frame pose differ-ences but miss the physical artifacts that matter most, such as unstablesupport and incorrect contacts (e.g., foot skating and mistimed touch-downs). Meanwhile, widely used test suites are small and lack the diver-sity needed to stress contact-rich, long-horizon behaviors. We introduce# Corresponding author.HumanTracker to make humanoid tracking evaluation both perceptu-ally aligned and scalable. HumanTracker contributes 150 hours of newlycaptured optical motion from 24 professional performers, organized intofour motion families with text labels for fine-grained diagnosis. We fur-ther propose HumanScore, a preference-aligned metric trained from 12Khuman-labeled motion pairs on synchronized tracking videos via a trajec-tory reward model. Across representative state-of-the-art trackers, Hu-manScore better predicts held-out human preferences and reveals contactand stability failures that kinematic metrics often miss. The project pageis available at https://dairuliu.github.io/humantracker.