ChingMu 1000 Hour Embodied Motion Dataset High precision optical motion capture data for humanoid robots, dexterous hands, embodied AI, and virtual production. : Duration 1000+ hours @ 120 Hz Scenarios 15+ real world scenes Tasks 500+ standardized tasks Objects 200+ tracked props (6D pose) Modalities Skeleton · Finger · Object 6D · Video · Labels Formats BVH · Retargeted CSV · NPZ ✅ Access note: This dataset is fully open and publicly accessible. Key Features Optical ground truth – sub mm accuracy, 120 fps, no estimation errors. Dexterous hands – 20+ DoF per hand, synchronized with object 6DOP pose. Robot ready – pre retargeted to Unitree G1; custom retargeting available. Real world diversity – 15+ scenarios, 500+ tasks, 200+ objects. Multi modal – full body skeleton, finger motion, object pose, multi view video, semantic labels. Quality assured – every take passes automated cleaning + manual inspection; quality flags provided. Dataset Summary ChingMu 1000H is an optical motion capture dataset designed for training and validating embodied AI and humanoid robot controllers. It covers full body skeleton, finger articulation, object 6D pose, multi view video, and semantic labels acros…
Runs entirely in your browser via DuckDB-Wasm — this dataset's real data file is loaded once, then queried locally. Nothing is sent to a server.
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