PHUMA: Physically-Grounded Humanoid Locomotion Dataset

TL;DR

PHUMA is a new physically-grounded humanoid locomotion dataset that improves motion imitation by addressing artifacts in large-scale video-based data, enabling more reliable and diverse humanoid behaviors.

Contribution

The paper introduces PHUMA, a physically-grounded humanoid dataset that enhances motion quality through careful data curation and physics-constrained retargeting, surpassing existing datasets.

Findings

PHUMA-trained policies outperform existing datasets in motion imitation tasks.

PHUMA enables stable imitation of diverse human motions.

The dataset improves path following and motion diversity in humanoid locomotion.

Abstract

Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture datasets such as AMASS, but these are scarce and expensive, limiting scalability and diversity. Recent studies attempt to scale data collection by converting large-scale internet videos, exemplified by Humanoid-X. However, they often introduce physical artifacts such as floating, penetration, and foot skating, which hinder stable imitation. In response, we introduce PHUMA, a Physically-grounded HUMAnoid locomotion dataset that leverages human video at scale, while addressing physical artifacts through careful data curation and physics-constrained retargeting. PHUMA enforces joint limits, ensures ground contact, and eliminates foot skating, producing motions that are both large-scale and physically reliable. We evaluated PHUMA in two sets of conditions: (i) imitation of unseen motion from self-recorded test videos and (ii) path following with pelvis-only guidance. In both cases, PHUMA-trained policies outperform Humanoid-X and AMASS, achieving significant gains in imitating diverse motions. The code is available at https://davian-robotics.github.io/PHUMA.