PHUMA: Physically-Grounded Humanoid Locomotion Dataset
Authors: name: Kyungmin Lee, name: Sibeen Kim, name: Minho Park, name: Hyunseung Kim, name: Dongyoon Hwang, name: Hojoon Lee, name: Jaegul Choo
Organization: DAVIAN Robotics

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.