Trim the card and fix badge layout

Readers arrive from the paper or the blog, so the card no longer restates the
capture protocol, calibration steps, annotation pipeline, or per-method
benchmark findings, all of which are covered better there. What remains is what
decides whether to request access: scale, modalities, the two capture scales,
the precision figures, and the access terms. Body drops from ~400 lines to ~170,
and from seven figures to one.

Badges were stacking because HF only exempts shields.io images from its
'a:has(img:only-child) { display: block }' rule when they are wrapped in a link;
the two unlinked ones broke the row. All four now link.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

README.md

@@ -126,53 +126,38 @@ extra_gated_button_content: Submit access request
   </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>
-    <img src="https://img.shields.io/badge/Technical_Report-Coming_Soon-6B7280" alt="Technical report coming soon">
+    <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>
-    <img src="https://img.shields.io/badge/Data_Files-Coming_Soon-6B7280" alt="Data files coming soon">
+    <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 transforms real home environments into spatially calibrated, temporally synchronized recording studios for embodied AI.</b>
+    <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">Watch the demo video</a>
+    ▶ <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/">Read the full story on the blog</a>
+    <a href="https://ace-data-engine.github.io/ACE-Data-0/">Full story, figures, and interactive examples on the blog</a>
   </p>
 </div>
 
-## Dataset Description
+## What this is
 
-### Dataset Summary
+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.
 
-Embodied intelligence faces a fundamental data bottleneck. 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 typically fragment this experience
-across viewpoints, modalities, or spatial scales, leaving the full perception-action loop only
-partially observed.
+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.
 
-We introduce the **A**mbient **C**apture **E**ngine (**ACE**), a human-centric data engine that
-transforms real home environments into spatially calibrated, temporally synchronized recording
-studios. ACE operates at two complementary scales:
-
-- **Table-scale capture** resolves fine-grained hand-object manipulation around work surfaces.
-- **Room-scale capture** preserves whole-body motion, locomotion, and interactions distributed
-  across a furnished home.
-
-Across both settings, ACE records egocentric and multi-view exocentric video, full-body and
-articulated hand motion, high-fidelity object geometry and 6-DoF trajectories, multi-channel audio,
-and tactile signals as a unified multisensory stream.
-
-Using ACE, we build **ACE-Data-0**, a large-scale dataset of natural, goal-directed household
-activity spanning atomic manipulation, long-horizon chains of household activities, and human-scene
-interaction.
-
-### Dataset Scale
-
-| Statistic | ACE-Data-0 |
+| | |
 | --- | ---: |
 | Recorded activity | 150 hours+ |
 | Video frames | 17M+ |
@@ -182,312 +167,108 @@ interaction.
 | 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 |
-| Raw data rate | approx. 1 TB per hour of session |
-
-These values describe the planned ACE-Data-0 release and will be verified in the final release
-manifest.
-
-An *episode* is a contiguous segment of interaction that realizes one meaningful sub-goal, the
-smallest unit that stays semantically self-contained as a training example. Episodes are counted
-within takes rather than recorded in isolation, so the actions preceding and following each segment
-are preserved in the same stream.
 
-### What Makes ACE-Data-0 Different
+These values describe the planned release and will be verified in the final release manifest.
 
-- **Nothing is fragmented.** Egocentric video, exocentric video, body and hand motion, object pose,
-  audio, and touch are recorded in one pass, on one clock, in one world frame, not assembled from
-  separate sources.
-- **Real homes, not laboratories.** The room-scale site is a fully furnished 200 m² apartment. The
-  clutter, furniture occlusion, and spatial constraints that lab capture removes are exactly what
-  makes home interaction hard, and they are kept.
-- **Long horizons.** Participants receive **goal-level** instructions ("prepare a cup of tea and
-  serve it at the table") rather than step-by-step scripts. Planning, hesitation, and improvisation
-  enter the data by themselves.
-- **Annotations are measured, not estimated.** Human states, object states, and contact are
-  metrically tracked or directly sensed. Pose reprojections, bounding boxes, motion trails, and
-  contact events follow by projection, with no estimation model in the loop. Only the textual
-  descriptions are generated, and those are human-verified.
+## What each take contains
 
-## Dataset Structure
+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.
 
-### Data Modalities
-
-Each take is organized around a shared timeline and a shared world coordinate frame.
-
-| Modality | Contents | Role |
-| --- | --- | --- |
-| Egocentric video | 4 fisheye views, IMU readings, per-frame 6-DoF headset pose from the tracked rig | Participant-centric perception |
-| Exocentric video | 8 synchronized fixed-camera views, each with intrinsics and world-frame pose | Scene, body, and object context |
-| Human motion | 41-joint body skeletons plus converted SMPL-X parameters | Metric kinematic supervision |
-| Hand motion | Articulated hand state and per-view projections | Dexterous manipulation supervision |
-| Object state | Scanned or 2DGS-reconstructed meshes, identity, 6-DoF pose at 60 Hz, motion trails | Dynamic scene-state supervision |
-| Audio | Synchronized multi-source audio from the exocentric cameras and the headset | Contact, appliance, and ambient events |
-| Tactile | Hand-shaped pressure grids with calibrated normalization and baseline correction | Physical interaction supervision |
-| Calibration | Camera intrinsics, extrinsics, headset pose, world registration, cross-stream timeline | Cross-view and cross-modal alignment |
-| Language | Goal- and activity-level descriptions | Semantic supervision |
-
-Because every take ships its calibration and its timeline as data, any tracked 3D point can be
-projected onto any pixel of any view, and any two streams can be paired at any instant. Modalities
-can therefore be combined freely for training, as inputs or as supervision, without rerunning any
-part of the capture pipeline.
-
-### Annotations
-
-| Type | Contents |
+| Modality | Contents |
 | --- | --- |
-| Object | category label, 2D/3D bounding boxes in all views, per-frame 6-DoF pose, mesh, motion trail |
-| Human | full-body pose per frame, reprojected as pixel-aligned 2D overlays in every view plus metric 3D in the world frame |
-| Hand | articulated finger configurations during dexterous manipulation |
-| Tactile | per-frame pressure reading, aligned to the visual streams and to the object currently in use |
-| Audio and language | sound-event descriptions, plus the take's goal and the sequence of sub-goals reaching it |
-| Calibration and sync | per-camera intrinsics, camera poses in the shared world frame, full cross-stream timeline |
+| 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 |
 
-### Activity Families
+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).
 
-| Take type | Content | Typical length |
-| --- | --- | --- |
-| **Atomic HOI** | 1-3 household tasks drawn from 15+ activity types (pouring water, drinking, making tea, watering plants, chopping vegetables, cooking, tidying up) | ~3 min |
-| **Chain of HOI** | the full range of short tasks combined into one continuous activity; sub-tasks interleave freely, and every take ends with the scene tidied back into order | ~20-30 min |
-| **Human-Scene Interaction (HSI)** | almost no objects; whole-body motion (walking, exercising) and human-scene contact (sitting, lying, leaning) with tables, chairs, and sofas | ~5 min |
-
-<div align="center">
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/script-example.webp" alt="Examples of ACE-Data-0 task categories and long-horizon capture scripts" width="88%">
-<br>
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/statistics.webp" alt="ACE-Data-0 task families and dataset statistics" width="88%">
-</div>
-
-## Dataset Creation
-
-### Capture System
+## How it was captured
 
 |  | Table-scale | Room-scale |
 | --- | --- | --- |
-| Space | ~30 m² workspace around a work desk | ~200 m² furnished apartment (kitchen, dining, living room, bedroom) |
-| Target | fine-grained dexterous hand-object manipulation | whole-body activity, locomotion, scene-wide interaction |
-| Exocentric RGB | 8 × GoPro at close range (0.3-0.5 m), 1920×1080 @ 30 FPS | 8 × ZED One on adjustable poles, 1920×1080 @ 30 FPS, at least 4 views on any point |
+| 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 | RANSAC triangulation of 2D keypoints from 8 exo views, plus manual refinement | Manus mocap gloves @ 60 Hz |
-| Objects | 25+ interactable instances, 8+ categories | 25+ interactable instances |
-
-**Shared across both sites**
-
-| Device | Role | Spec |
-| --- | --- | --- |
-| ACE-Ego-Head-V02 Lite | Egocentric capture | 4 fisheye cameras (front/rear, L/R), 1088×1280 @ 20 FPS, onboard IMU, 5 tracked markers |
-| OptiTrack PrimeX 22 | Optical motion capture | 2048×1088 @ 60 Hz; 41 body markers per participant, plus objects and the ego rig |
-| ACE-Sense-Glove Lite | Contact pressure | full-palm pressure map, both hands |
-| GoPro / ACE-Ego-Head | Audio | contact events, appliance operation, ambient scene sound |
-
-<table align="center" width="100%">
-  <tr>
-    <td width="50%" align="center"><img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/setup-ace-t.webp" width="100%" alt="Table-scale configuration"></td>
-    <td width="50%" align="center"><img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/setup-ace-r.webp" width="100%" alt="Room-scale configuration"></td>
-  </tr>
-  <tr>
-    <td align="center"><sub><b>Table-scale configuration</b></sub></td>
-    <td align="center"><sub><b>Room-scale configuration</b></sub></td>
-  </tr>
-</table>
-
-### Capture Protocol
-
-Participants receive household goals rather than fixed sequences of atomic actions. They choose
-routes, object instances, grasp strategies, subtask order, pauses, and recovery behavior. This
-preserves natural variation while keeping the activity goal and the measured physical state
-available as supervision. Each participant completes all designed tasks across a 2-day session.
-
-Every session follows the same five-step protocol at both sites:
-
-1. **Scene preparation.** Objects are placed at randomized yet plausible initial positions.
-2. **Participant setup.** The participant puts on the mocap suit, the headset, and the gloves, then
-   performs a short T-pose routine that registers their skeleton with the tracking system.
-3. **Task briefing.** The participant receives a goal-level instruction verbally; how to achieve it
-   is left entirely to them.
-4. **Recording.** The take opens with the clock glance described below, after which all sensors
-   record continuously while an operator monitors stream health on a live dashboard.
-5. **Post-checks.** Synchronization and tracking quality are verified after each take, and failed
-   takes are flagged for re-capture.
-
-<div align="center">
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/data-procedure.webp" alt="ACE capture, temporal synchronization, and annotation workflow" width="88%">
-</div>
-
-### Temporal Synchronization
-
-All devices are registered to the OptiTrack 60 Hz clock, which internally delivers strictly
-simultaneous frames. Because recorded camera timestamps lag true exposure by approximately 0.29 s
-and drift slowly, we instead let each camera **photograph a clock**: the mocap host displays its own
-time as a nanosecond-resolution QR code, and readings sampled across a take are fitted with a
-constant offset plus a slow drift.
-
-- Residual alignment error: **millisecond level** (within a single mocap frame).
-- Egocentric fisheye views, mutual misalignment: **< 2 ms**.
-- Independent check, per-camera offsets re-estimated during calibration: **< 4 ms**.
-- Tactile gloves are aligned to the headset by matching correlated IMU motion templates.
-
-Every take ships a table mapping each camera frame, egocentric and exocentric alike, to its 60 Hz
-mocap frame.
-
-### Spatial Calibration
+| Hand pose | triangulated from 8 exo views, manually refined | Manus mocap gloves @ 60 Hz |
 
-The exocentric cameras never move but barely share a view; the egocentric cameras move every frame.
-Both are solved through the motion-capture system rather than through co-visibility.
+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.
 
-- **Exocentric.** An ArUco board with a retroreflective marker at each corner is visible to the RGB
-  cameras and the infrared cameras at once, bridging cameras that share no field of view. Median
-  reprojection error on held-out frames: **< 3 px**.
-- **Egocentric.** The 5 markers on the headset chassis form a rigid body tracked at 60 Hz, so the
-  only unknown is the fixed camera-to-body transform, solved as hand-eye calibration and refined by
-  joint bundle adjustment. Median reprojection error: **approx. 2 px**. Camera poses are therefore
-  *measured* rather than estimated, and do not drift.
+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.
 
-Calibration is re-verified twice a day. This alignment lets one modality supervise another without
-uncertain temporal or spatial correspondence.
-
-### Annotation Process
-
-Most annotations are derived from the synchronized and calibrated capture system rather than
-estimated independently from video. Because the human and object annotations come from measured 3D
-states rather than image-based detectors, they remain correct where detectors typically fail: under
-furniture occlusion, extreme viewpoints, and motion blur.
-
-Textual descriptions are the one generated type. Gemini-3.1-pro-preview watches the ego-view video
-and describes each time span in natural language, segment by segment; human annotators then check
-the auto-generated labels and correct the descriptions by hand.
-
-<div align="center">
-<b>Human pose annotation</b><br>
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/annotation-smplx-sg.webp" alt="Human pose annotation across synchronized views and 3D motion" width="88%">
-<br><br>
-<b>Object state annotation</b><br>
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/annotation-obj-sg.webp" alt="Object state annotation across synchronized views and 3D motion" width="88%">
-<br><br>
-<b>Articulated hand annotation</b><br>
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/annotation-hand.webp" alt="Articulated hand annotation" width="88%">
-<br><br>
-<b>Tactile annotation</b><br>
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/annotation-tactile.webp" alt="Tactile annotation" width="88%">
-<br><br>
-<b>Language annotation</b><br>
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/annotation-caption.webp" alt="Language annotation" width="88%">
-</div>
-
-### Source Data
-
-ACE-Data-0 consists of original recordings collected with the ACE capture system. It is not
-assembled from scraped internet video or third-party household datasets.
+The blog and the technical report cover the capture protocol, calibration, and annotation pipeline
+in full.
 
 ## Benchmark
 
-<div align="center">
-<img src="https://ace-data-engine.github.io/ACE-Data-0/assets/images/benchmark-steps.webp" alt="Three-level benchmark: signals, scene components, interactions" width="88%">
-</div>
-
-We hold out **10 hours** of capture as a test set and evaluate **30+ published methods** with their
-officially released checkpoints, across three levels that mirror the capabilities an embodied agent
-must chain together: sensing contact, estimating scene state, mastering hand-object coordination.
-
-**1. Low-level signals: tactile from vision.** Predict full-hand grasp pressure from egocentric
-video, evaluated against synchronized glove measurements (temporal accuracy, contact IoU, volumetric
-IoU, center-of-pressure error). *Finding:* detecting **when** contact occurs is far easier than
-recovering **where** pressure is distributed. The best method's absolute C-IoU and V-IoU remain
-modest, and a substantial generalization gap persists.
-
-**2. Scene components: human motion estimation.** 22 methods across per-frame, temporal,
-scene-aware, single-view exo, multi-view exo, and egocentric settings, all against the same metric
-ground truth. *Findings:* (i) strong Procrustes-aligned pose accuracy does **not** imply an accurate
-world-frame trajectory, and the two rankings differ; (ii) scene context mainly helps locate the body
-in the room rather than improve joint configuration; (iii) egocentric methods lag because much of
-the body is out of frame.
-
-**3. Embodied interaction: hand motion from ego and exo views.** The same interactions are recorded
-simultaneously from both viewpoints, so the comparison changes only the viewpoint. *Findings:*
-exocentric methods reach approximately 9.1 mm PA-MPJPE and a 63 mm world-frame trajectory error,
-while egocentric world-space methods remain near 100 mm. This points at **egomotion estimation**,
-not finger articulation, as the dominant error source in egocentric hand reconstruction.
+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.
 
-Full tables, metric definitions, and per-method results are in the technical report.
+## Access
 
-## Considerations for Using the Data
-
-### Intended Uses
-
-ACE-Data-0 is intended for non-commercial academic research on:
-
-- Embodied perception and interaction understanding
-- Human and hand motion recovery
-- Human-object and human-scene interaction
-- Multi-view and egocentric video understanding
-- Cross-modal learning across vision, motion, audio, and touch
-- Object-state estimation and dynamic 3D scene understanding
-- Imitation learning, world models, and vision-language-action systems
-
-### Out-of-Scope and Prohibited Uses
-
-The dataset is not intended for:
-
-- Identifying, re-identifying, or profiling participants
-- Biometric recognition or surveillance
-- Inferring sensitive personal attributes
-- Training systems for harmful, deceptive, or privacy-invasive applications
-- Any commercial purpose
-- Representing all homes, cultures, bodies, abilities, or household practices without further
-  validation
-
-The [ACE-Data-0 Research License Agreement](./LICENSE) is binding and defines the full set of
-restrictions.
-
-### Personal and Sensitive Information
-
-All participants volunteered and signed a written informed consent form covering data collection and
-research release, including the appearance of their faces. The dataset contains identifiable
-individuals. Any attempt to identify, contact, locate, or infer private attributes of a participant,
-and any use of the data for biometric identification, surveillance, or profiling, is prohibited by
-the license and will result in access being revoked.
+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.
 
-If you are a participant and wish to have your recordings withdrawn, reach the authors through the
-Community tab of this repository or through the project page, and the affected takes will be removed
-from subsequent releases.
+- 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.
 
-### Known Limitations
+> **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.
 
-- **Site coverage.** Two sites only, hence limited variation in layouts, furnishings, and lighting.
-- **Instrumented ground truth.** Tracked objects must be scanned and marked in advance. State
-  changes of articulated mechanisms, fluids, and deformable materials are not annotated.
-- **Visible instrumentation.** The mocap suit, gloves, headset, and markers appear in the recordings
-  and may introduce dataset-specific visual cues.
+## Intended and prohibited uses
 
-## Access and Download
+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.
 
-This repository is **gated**. Access is granted to named individuals for **non-commercial academic
-research only**.
+**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.
 
-1. Sign in to Hugging Face and submit the access form on this page. Access is granted once you
-   accept the terms.
-2. Use your **institutional** email and describe your intended research. What you submit is recorded
-   as part of your licence, and inaccurate information is a breach of the agreement.
-3. 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.
+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.
 
-For commercial licensing or industrial collaboration, open a thread on the Community tab of this
-repository instead of submitting the form.
+## Limitations
 
-> **Status.** Data files are not published yet. This card describes the dataset and its capture
-> system; repository organization, storage requirements, checksums, and loading examples will be
-> added at release time. Because the dataset comprises large synchronized media and sensor streams,
-> the release will use sharded archives rather than direct browser downloads. Approved users retain
-> access across the release.
+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
 
-Released under the [ACE-Data-0 Research License Agreement](./LICENSE): non-commercial academic
-research use only, no redistribution, no re-identification. Please read it in full before requesting
-access.
+[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
 
-If you use ACE-Data-0 in your research, please cite:
-
 ```bibtex
 @article{cao2026acedata0,
   title   = {ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine},
@@ -500,14 +281,9 @@ If you use ACE-Data-0 in your research, please cite:
 }
 ```
 
-The arXiv identifier above is a placeholder and will be replaced when the technical report is
-posted.
+The arXiv identifier is a placeholder and will be replaced when the report is posted.
 
 ## Contact
 
-For project updates, visit the [ACE-Data-0 blog](https://ace-data-engine.github.io/ACE-Data-0/),
-which hosts the full story, the audio-enabled demo, interactive multi-camera examples, and a Chinese
-version.
-
-Questions about access, licensing, or annotations should be raised on the Community tab of this
-repository.
+Project updates on the [blog](https://ace-data-engine.github.io/ACE-Data-0/). Questions about
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