| --- |
| pretty_name: ACE-Data-0 |
| license: other |
| license_name: ace-data-0-research-license |
| license_link: LICENSE |
| viewer: false |
| language: |
| - en |
| task_categories: |
| - robotics |
| - keypoint-detection |
| - video-classification |
| - audio-classification |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - video |
| - audio |
| - 3d |
| - timeseries |
| - robotics |
| - embodied-ai |
| - multimodal |
| - egocentric |
| - multi-view |
| - motion-capture |
| - tactile-sensing |
| - human-object-interaction |
| - human-scene-interaction |
| - long-horizon |
| - smpl-x |
| - mano |
| - imitation-learning |
| - vision-language-action |
| extra_gated_heading: Request access to ACE-Data-0 |
| extra_gated_description: >- |
| ACE-Data-0 contains identifiable recordings of human participants captured |
| inside real homes. Access is granted to named individuals for non-commercial |
| academic research only, on acceptance of the terms below. |
| extra_gated_prompt: >- |
| **Before requesting access, please read the following.** |
| |
|
|
| ACE-Data-0 is released **exclusively for non-commercial academic research** |
| under the [ACE-Data-0 Research License |
| Agreement](https://huggingface.co/datasets/ACERobotics/ACE-Data-0/blob/main/LICENSE). |
| By submitting this form you confirm that you have read that agreement and that |
| you agree to be bound by it. |
|
|
|
|
| The dataset contains **video, audio, motion, and tactile recordings of |
| identifiable human participants** who volunteered and gave informed consent |
| for research release. In particular, you agree that you will **not**: |
|
|
|
|
| - use the data, or anything derived from it, for any commercial purpose; |
|
|
| - redistribute, publish, or otherwise share the data with anyone who has not |
| been granted access individually through this form; |
|
|
| - attempt to identify, contact, locate, or infer private attributes of any |
| participant appearing in the recordings; |
|
|
| - use the data to develop or evaluate biometric identification, surveillance, |
| or profiling systems. |
|
|
|
|
| Access is granted to **you personally and is not transferable**. Every |
| colleague, student, or collaborator who needs the data must submit their own |
| request. |
|
|
|
|
| The details you provide below are recorded as part of your licence. Submitting |
| inaccurate information, or using the dataset outside the terms above, is a |
| breach of the agreement and grounds for revoking your access. |
|
|
|
|
| Commercial licensing, industrial collaboration, and any use outside the scope |
| above are handled separately. Please open a thread on the Community tab of this |
| repository rather than submitting this form. |
| extra_gated_fields: |
| Full name: text |
| Institutional email address: text |
| Institution or organization: text |
| Country: country |
| Position: |
| type: select |
| options: |
| - Undergraduate student |
| - Master's student |
| - PhD student |
| - Postdoctoral researcher |
| - Faculty / Principal investigator |
| - Research scientist / Research engineer |
| - label: Other |
| value: other |
| Homepage, Google Scholar, or lab page: text |
| Which parts of ACE-Data-0 do you intend to use?: |
| type: select |
| options: |
| - Egocentric video |
| - Exocentric video |
| - Human body and hand motion |
| - Object meshes and 6-DoF poses |
| - Audio |
| - Tactile |
| - Annotations only |
| - The complete dataset |
| Describe your intended research use in at least two sentences (project, tasks, and expected outputs): text |
| I confirm that I am requesting access for non-commercial academic research only: checkbox |
| I have read and agree to the ACE-Data-0 Research License Agreement: checkbox |
| I will not redistribute the dataset or any part of it, and I will direct colleagues to submit their own request: checkbox |
| I will not attempt to identify participants, nor use the data for biometric identification, surveillance, or profiling: checkbox |
| I agree to cite ACE-Data-0 in any publication or public artifact that uses it: checkbox |
| I understand that access is personal, non-transferable, and may be revoked: checkbox |
| extra_gated_button_content: Submit access request |
| --- |
| |
| <div align="center"> |
| <h1>ACE-Data-0</h1> |
| <h3>Human-Centric Ambient Capture as Embodied Data Engine</h3> |
| <p> |
| <b>S-Lab, Nanyang Technological University, Singapore</b> |
| · |
| <b>ACE Robotics</b> |
| </p> |
| <p> |
| <a href="https://ace-data-engine.github.io/ACE-Data-0/"><img src="https://img.shields.io/badge/Blog-ACE--Data--0-0054A6?logo=googlechrome&logoColor=white" alt="Blog"></a> |
| <a href="https://arxiv.org/abs/2607.28625"><img src="https://img.shields.io/badge/arXiv-2607.28625-B31B1B?logo=arxiv&logoColor=white" alt="Technical report on arXiv"></a> |
| <a href="./LICENSE"><img src="https://img.shields.io/badge/License-Research_Only-A33B20" alt="Research-only license"></a> |
| <a href="https://ace-data-engine.github.io/ACE-Data-0/"><img src="https://img.shields.io/badge/Data_Files-Coming_Soon-6B7280" alt="Data files coming soon"></a> |
| </p> |
| <p> |
| <img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/data-teaser.webp" width="100%" alt="ACE-Data-0 teaser: table-scale and room-scale ambient capture with synchronized multi-modal streams"> |
| </p> |
| <p> |
| <b>ACE turns real home environments into spatially calibrated, temporally synchronized recording studios for embodied AI.</b> |
| </p> |
| <p> |
| ▶ <a href="https://ace-data-engine.github.io/ACE-Data-0/assets/videos/teaser-video.mp4">Demo video</a> |
| · |
| <a href="https://ace-data-engine.github.io/ACE-Data-0/">Full story, figures, and interactive examples on the blog</a> |
| </p> |
| </div> |
| |
| ## What this is |
|
|
| Learning to act in the physical world requires more than observing what an action looks like: models |
| must capture how first-person perception, whole-body motion, dexterous manipulation, object state, |
| sound, and touch evolve **together** as humans pursue goals over time. Existing datasets fragment |
| this experience across viewpoints, modalities, or spatial scales. |
|
|
| ACE-Data-0 records all of it in one pass, on one clock, in one world frame. Participants receive |
| **goal-level** instructions ("prepare a cup of tea and serve it at the table") rather than |
| step-by-step scripts, so planning, hesitation, and improvisation enter the data by themselves. Human |
| states, object states, and contact are metrically tracked or directly sensed, so the annotations are |
| **measured rather than estimated**: they stay correct under furniture occlusion, extreme viewpoints, |
| and motion blur, where image-based detectors fail. |
|
|
| | | | |
| | --- | ---: | |
| | Recorded activity | 150 hours+ | |
| | Video frames | 17M+ | |
| | Interaction episodes | 75,000+ | |
| | Task categories | 200+ | |
| | Participants | 50+ | |
| | Capture environments | 2 | |
| | Views per moment | 8+ exocentric, plus 4 egocentric fisheye | |
| | Take length | minutes, not seconds; up to 20-30 min for long-horizon chains | |
|
|
| These values describe the planned release and will be verified in the final release manifest. |
|
|
| ## What each take contains |
|
|
| Every take shares one timeline and one world coordinate frame, and ships its own calibration and |
| sync tables as data. Any tracked 3D point can therefore be projected onto any pixel of any view, and |
| any two streams paired at any instant, without rerunning any part of the capture pipeline. |
|
|
| | Modality | Contents | |
| | --- | --- | |
| | Egocentric video | 4 fisheye views @ 20 FPS, IMU, per-frame 6-DoF headset pose from the tracked rig | |
| | Exocentric video | 8 synchronized views @ 30 FPS, each with intrinsics and world-frame pose | |
| | Human motion | 41-joint skeletons, articulated hand poses, converted SMPL-X parameters | |
| | Object state | scanned or 2DGS meshes, 6-DoF pose @ 60 Hz, 2D/3D boxes in all views, motion trails | |
| | Audio | multi-source, from the exocentric cameras and the headset | |
| | Tactile | full-palm pressure grids, normalized and baseline-corrected | |
| | Language | per-segment activity descriptions, take goal, and its sequence of sub-goals | |
|
|
| Takes come in three families: **atomic HOI** (1-3 household tasks, ~3 min), **chains of HOI** (one |
| continuous activity of ~20-30 min ending with the scene tidied back into order), and **human-scene |
| interaction** (whole-body motion and furniture contact, almost no objects, ~5 min). |
|
|
| ### Room-scale directory structure |
|
|
| ```text |
| Room-scale/ |
| ├── Body_shape/ |
| ├── calibration/ |
| └── Take-xxxxxx/ |
| ├── motions/ |
| ├── raw/ |
| ├── timeline/ |
| ├── videos_and_annotations/ |
| └── caption/ |
| ``` |
|
|
| - `Body_shape/` contains the body-shape beta parameters for each participant. |
| - `calibration/` contains calibration data for the OptiTrack system and ZED cameras, together with |
| the scripts used to calculate the Homie calibrations. |
| - Each `Take-xxxxxx/` directory contains the data for one take: |
| - `motions/` contains human and object motion data. |
| - `raw/` contains the raw egocentric, exocentric, and motion-capture data. |
| - `timeline/` contains the synchronized timeline for all modalities. Its `annotation.json` file |
| provides `video_keep_interval_s`, which specifies the video intervals to retain after removing |
| footage captured only for synchronization. |
| - `videos_and_annotations/` contains the captured egocentric and exocentric videos at their |
| original resolution, along with annotated videos for the human body, hands, and objects. |
| - `caption/` contains a text description of the take. Caption timestamps refer to the filtered |
| timeline after applying `video_keep_interval_s`. |
| |
| ### Table-scale directory structure |
|
|
| ```text |
| Table-scale/ |
| ├── HOI/ |
| │ ├── data/ |
| │ │ └── chunk-000/ |
| │ ├── meta/ |
| │ │ └── takes/ |
| │ ├── objs/ |
| │ ├── raw/ |
| │ │ ├── calibration/ |
| │ │ ├── camera_array_aniposelib/ |
| │ │ ├── captions/ |
| │ │ ├── hand_poses/ |
| │ │ ├── imu/ |
| │ │ ├── obj_file/ |
| │ │ ├── obj_poses/ |
| │ │ ├── optitrack_to_camera_world_alignment/ |
| │ │ └── poses/ |
| │ └── videos/ |
| │ ├── egocap/ |
| │ └── gopro/ |
| └── Tactile/ |
| └── ACE-Data-Tactile-{0..10}/ |
| ├── data/ |
| │ └── chunk-000/ |
| ├── meta/ |
| │ └── takes/ |
| ├── raw/ |
| │ ├── calibration/ |
| │ ├── camera_array_aniposelib/ |
| │ ├── captions/ |
| │ ├── imu/ |
| │ └── tactile/ |
| └── videos/ |
| ├── egocap/ |
| └── gopro/ |
| ``` |
|
|
| - `HOI/` contains table-scale human-object interaction recordings focused on fine-grained |
| hand-object manipulation. |
| - `data/` contains LeRobot-compatible parquet chunks. Rows are canonical reference samples on a |
| fixed-rate timeline; `frame_index` is an episode-local reference-sample index, while the raw-row |
| ranges point to records in `raw/`. |
| - `meta/` contains take-level metadata, including the `takes/` index and task information. |
| - `objs/` contains the object mesh assets referenced by the object-tracking data. |
| - `raw/` contains the captured sensor records and sidecars, including camera calibration, |
| camera-array processing data, captions, human and object poses, IMU data, object files, and |
| OptiTrack-to-camera alignment data. |
| - `videos/` contains the source video streams, separated into `egocap/` for egocentric capture and |
| `gopro/` for exocentric GoPro cameras. |
| |
| - `Tactile/` contains the tactile table-scale release split into eleven shards, |
| `ACE-Data-Tactile-0/` through `ACE-Data-Tactile-10/`. Each shard follows the same layout: |
| - `data/` contains the canonical timeline samples and parquet chunks for that shard. |
| - `meta/` contains take-level metadata under `takes/`. |
| - `raw/` contains calibration, camera-array processing data, captions, IMU records, and raw |
| tactile measurements under `tactile/`. |
| - `videos/` contains the corresponding `egocap/` and `gopro/` video streams. |
| |
| The `data/`, `raw/`, and `videos/` directories are complementary: the parquet data provides timeline |
| indices and raw-row ranges, sensor records remain in `raw/`, and the video bytes remain in `videos/`. |
|
|
| ## How it was captured |
|
|
| | | Table-scale | Room-scale | |
| | --- | --- | --- | |
| | Space | ~30 m² desk workspace | ~200 m² furnished apartment | |
| | Target | fine-grained hand-object manipulation | whole-body activity and locomotion | |
| | Exocentric RGB | 8 × GoPro at 0.3-0.5 m | 8 × ZED One, at least 4 views on any point | |
| | Optical mocap | 16 × OptiTrack PrimeX 22 | 12 × OptiTrack PrimeX 22 | |
| | Hand pose | triangulated from 8 exo views, manually refined | Manus mocap gloves @ 60 Hz | |
|
|
| Participants wear an ACE-Ego-Head-V02 Lite headset (4 fisheye cameras, IMU, 5 tracked markers), a |
| 41-marker mocap suit, and full-palm tactile gloves. |
|
|
| Two numbers carry the credibility of everything above. **Temporal:** all devices are registered to |
| the OptiTrack 60 Hz clock by photographing a nanosecond-resolution clock displayed on the mocap |
| host, giving millisecond-level residuals, within a single mocap frame. **Spatial:** an ArUco board |
| with retroreflective corners bridges exocentric cameras that share no field of view (median |
| reprojection error < 3 px), while the headset is solved by hand-eye calibration against its tracked |
| rig (~2 px), so egocentric camera poses are measured rather than estimated and do not drift. |
|
|
| The blog and the technical report cover the capture protocol, calibration, and annotation pipeline |
| in full. |
|
|
| ## Benchmark |
|
|
| We hold out 10 hours as a test set and evaluate 30+ published methods across three levels: tactile |
| inference from video, human motion recovery, and hand motion from egocentric and exocentric views. |
| Existing methods degrade sharply under contact, occlusion, egomotion, and long horizons. In |
| particular, strong per-frame pose accuracy does not imply an accurate world-frame trajectory, and |
| egomotion, not finger articulation, dominates the error in egocentric hand reconstruction. Full |
| tables are in the technical report. |
|
|
| ## Access |
|
|
| This repository is **gated**. Access is granted to named individuals for **non-commercial academic |
| research only**, and takes effect once you accept the terms on the access form above. |
|
|
| - Use your **institutional** email and describe your intended research. What you submit is recorded |
| as part of your licence; inaccurate information is a breach of the agreement. |
| - Access is **personal and non-transferable**. Collaborators and students must each submit their own |
| request. Redistributing the data terminates your licence and revokes your access. |
| - For commercial licensing or industrial collaboration, open a thread on the Community tab instead |
| of submitting the form. |
|
|
| > **Data files are not published yet.** Repository layout, storage requirements, checksums, and |
| > loading examples will be added at release time. Because the streams are large, the release will |
| > use sharded archives rather than direct browser downloads. Approved users keep their access. |
|
|
| ## Intended and prohibited uses |
|
|
| Intended for non-commercial academic research on embodied perception, human and hand motion |
| recovery, human-object and human-scene interaction, egocentric and multi-view video understanding, |
| cross-modal learning across vision, motion, audio, and touch, and imitation learning, world models, |
| and vision-language-action systems. |
|
|
| **Not** for identifying, re-identifying, or profiling participants; biometric recognition or |
| surveillance; inferring sensitive personal attributes; any commercial purpose; or representing all |
| homes, cultures, bodies, abilities, or household practices without further validation. The |
| [LICENSE](./LICENSE) is binding and defines the full set of restrictions. |
|
|
| All participants volunteered and signed informed consent covering data collection and research |
| release, including the appearance of their faces. **The dataset contains identifiable individuals.** |
| If you are a participant and want your recordings withdrawn, reach us through the Community tab and |
| the affected takes will be removed from subsequent releases. |
|
|
| ## Limitations |
|
|
| Two sites only, so limited variation in layouts, furnishings, and lighting. Tracked objects must be |
| scanned and marked in advance, and state changes of articulated mechanisms, fluids, and deformable |
| materials are not annotated. The mocap suit, gloves, headset, and markers are visible in the |
| recordings and may introduce dataset-specific visual cues. |
|
|
| ## License |
|
|
| [ACE-Data-0 Research License Agreement](./LICENSE): non-commercial academic research only, no |
| redistribution, no re-identification. Read it in full before requesting access. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{cao2026acedata0, |
| title = {ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine}, |
| author = {Cao, Yukang and Xie, Haozhe and Wen, Beichen and Yao, Runmao and |
| Liu, Yinghao and Huang, Yue and Liao, Zhichao and Wang, Yunxiang and |
| Liu, Haiheng and Tian, Xingshun and Su, Dawei and Zhuo, Long and |
| Tao, Dacheng and Wang, Xiaogang and Pan, Liang and Liu, Ziwei}, |
| journal = {arXiv preprint arXiv:2607.28625}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Project updates on the [blog](https://ace-data-engine.github.io/ACE-Data-0/). Questions about |
| access, licensing, or annotations belong on the Community tab of this repository. |
|
|
