HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

2026-08-13

AI Summary

Existing evaluations of humanoid motion tracking rely primarily on kinematic errors, which fail to capture physically implausible distortions perceptible to humans—such as foot sliding or incorrect contact—and suffer from small-scale, low-diversity test sets. To address these limitations, this work introduces HumanTracker, a large-scale and diverse benchmark comprising 153 hours of optical motion capture data from professional actors, covering four action categories with accompanying textual annotations. Furthermore, the authors propose HumanScore, a human-aligned evaluation metric derived from a preference model trained on 12K motion pairs (24K individual motions). HumanScore enables fine-grained diagnosis of critical physical properties like contact fidelity and support stability, significantly outperforming conventional metrics across multiple state-of-the-art trackers, accurately predicting human preferences, and uncovering previously overlooked physical inconsistencies.

Abstract

Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect contacts such as foot skating and mistimed touch-downs. Meanwhile, widely used test suites are small and lack the diversity needed to stress contact-rich, long-horizon behaviors. We introduce HumanTracker to make humanoid tracking evaluation both perceptually aligned and scalable. The HumanTracker benchmark contains approximately 153 hours of optical motion trajectories from multiple professional performers, organized into four motion families with text labels for fine-grained diagnosis. We further propose HumanScore, a preference-aligned metric trained on 12K motion pairs containing 24K motions. Across representative state-of-the-art trackers, HumanScore better predicts human preferences and reveals contact and stability failures that kinematic metrics often miss.

Problem

• humanoid motion tracking
• perceptual alignment
• contact artifacts
• evaluation benchmark
• kinematic errors

Innovation

• Human-aligned evaluation
• motion tracking benchmark
• contact-aware metrics
• HumanScore
• humanoid teleoperation