Movens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos
Overview
This research presents Movens-Zero, a scalable architecture for humanoid motion foundation models trained on a large-scale dataset of physically executable motions derived from internet videos. The system supports joint learning across text, speech, music, and video modalities to enable real-time closed-loop execution. Experiments demonstrate consistent gains in generation and execution quality when scaling both the volume of verified data and model capacity.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported duration: 10000 hours · Basis not reported
“y, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities”
Exact source · revision 1Source owner not reported
Reported quantity · condition-motion pairs: 8192 other · reported trials
“chmark with 8,192 expert-verified condition-motion pairs across text, speech, music, and video. Experiments show consistent gains across all four modalities when scaling execution-verified data from 1% to 100% and action-expert capacity from 64M to 911M parameters, while demonstrating that data quality and cross-modal supervision improve generation and execution.”
Exact source · revision 1Source owner not reported
Dataset · modalities: text · Differing source assertions
“y, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities”
Exact source · revision 1Source owner not reported
Dataset · modalities: RGB video · Differing source assertions
“y, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities”
Exact source · revision 1Source owner not reported
Dataset · modalities: speech · Differing source assertions
“y, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities”
Exact source · revision 1Source owner not reported
Dataset · modalities: music · Differing source assertions
“y, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities”
Exact source · revision 1Source owner not reported
Dataset · embodiments: humanoid
“arXiv:2610.41877v1 Announce Type: new Abstract: Scaling humanoid motion foundation models is constrained by the scarcity of diverse, robot-executable data and by the difficulty of jointly learning from heterogeneous multimodal inputs while supporting real-time closed-loop control. We present Movens-5M, constructed from raw Internet videos through clip generation, quality filtering, semantic annotation, motion recover”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMovens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos
This research presents Movens-Zero, a scalable architecture for humanoid motion foundation models trained on a large-scale dataset of physically executable motions derived from internet videos. The system supports joint learning across text, speech, music, and video modalities to enable real-time closed-loop execution. Experiments demonstrate consistent gains in generation and execution quality when scaling both the volume of verified data and model capacity.
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