README.md

---
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>
    &nbsp;·&nbsp;
    <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&amp;logoColor=white" alt="Blog"></a>
    <a href="https://ace-data-engine.github.io/ACE-Data-0/"><img src="https://img.shields.io/badge/Technical_Report-Coming_Soon-6B7280" alt="Technical report coming soon"></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>
    &nbsp;·&nbsp;
    <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).

## 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:2607.00000},
  year    = {2026}
}
```

The arXiv identifier is a placeholder and will be replaced when the report is posted.

## 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.