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ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine

Research Embodied AI

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Representative image for ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine

Merged summary

TL;DR - ACE-Data-0 is a synchronized multisensory dataset capturing human activity at table and room scales. It addresses fragmented embodied-AI supervision for imitation learning, world models, and vision-language-action systems.

  • Contains 150 hours, 17 million video frames, and 75,000 episodes across 200 tasks performed by 50 participants.
  • Aligns egocentric and exocentric video with body and hand motion, object geometry and trajectories, audio, and tactile signals.
  • Covers fine-grained manipulation, long-horizon household activities, locomotion, and human-scene interaction.
  • Its hierarchical benchmark reveals limitations of current methods under contact, occlusion, egomotion, and long temporal horizons.

Sources (1)

ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine

arXiv cs.CV Yukang Cao, Haozhe Xie, Beichen Wen, Runmao Yao, Yinghao Liu, Yue Huang, Zhichao Liao, Yunxiang Wang, Haiheng Liu, Xingshun Tian, Dawei Su, Long Zhuo, Dacheng Tao, Xiaogang Wang, Liang Pan, Ziwei Liu 2026-07-30 arXiv:2607.28625
Public signals Hugging Face upvotes 46
Providers: Hugging Face · Upvotes 46 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-31 14:29:53.962303 UTC

TL;DR - ACE-Data-0 is a synchronized multisensory dataset capturing human activity at table and room scales. It addresses fragmented embodied-AI supervision for imitation learning, world models, and vision-language-action systems.

  • Contains 150 hours, 17 million video frames, and 75,000 episodes across 200 tasks performed by 50 participants.
  • Aligns egocentric and exocentric video with body and hand motion, object geometry and trajectories, audio, and tactile signals.
  • Covers fine-grained manipulation, long-horizon household activities, locomotion, and human-scene interaction.
  • Its hierarchical benchmark reveals limitations of current methods under contact, occlusion, egomotion, and long temporal horizons.
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