PhysDrift generates executable humanoid co-speech motions directly from speech via robot-native data curated by IK-EER, claiming better alignment and plausibility than human-centric retargeting.
PHUMA: Physically Reliable Humanoid Locomotion Dataset
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
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 suffer from physical artifacts such as floating, penetration, and foot skating, which hinder stable imitation. To address this, we introduce PHUMA, a Physically Reliable HUMAnoid locomotion dataset produced by a two-stage pipeline combining physics-aware curation and physics-constrained retargeting, aggregating both motion capture and internet video into a physically reliable, 73-hour corpus. On motion tracking benchmarks, PHUMA-trained policies achieve higher success rates than those trained on AMASS and Humanoid-X, and successfully transfer zero-shot to a real Unitree G1. The code is available at https://davian-robotics.github.io/PHUMA.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A data-centric approach shows that less than 3% of AMASS motion data, filtered by physics feasibility, diversity, and complexity, yields better humanoid tracking policies than the full dataset.
Humanoid-GPT is a causal Transformer pre-trained on a unified billion-scale motion dataset that tracks dynamic behaviors with zero-shot generalization to unseen motions and tasks.
Human2Humanoid is an unsupervised motion retargeting framework using CycleGAN, skeleton-aware GCN, end-effector consistency loss, and physics-aware constraints to transfer human motions to humanoid robots without paired data.
citing papers explorer
-
PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation
PhysDrift generates executable humanoid co-speech motions directly from speech via robot-native data curated by IK-EER, claiming better alignment and plausibility than human-centric retargeting.