PHUMA: Physically-Grounded Humanoid Locomotion Dataset
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.
6904bbe336711a7e91ed4d94
Kyungmin Lee
claimed_verified
2025-11-03T20:54:32.642Z
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Kyungmin Lee
Kyungminn
user
Kyungminn
6904bbe336711a7e91ed4d95
Sibeen Kim
6904bbe336711a7e91ed4d96
Minho Park
6904bbe336711a7e91ed4d97
Hyunseung Kim
6904bbe336711a7e91ed4d98
Dongyoon Hwang
6904bbe336711a7e91ed4d99
Hojoon Lee
6904bbe336711a7e91ed4d9a
Jaegul Choo
PHUMA: Physically-Grounded Humanoid Locomotion Dataset
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.
PHUMA, a large-scale and physically reliable humanoid locomotion dataset, improves motion imitation by addressing physical artifacts in human video data.
motion imitation humanoid locomotion motion capture datasets AMASS Humanoid-X physical artifacts floating penetration foot skating data curation physics-constrained retargeting joint limits ground contact path following pelvis-only guidance