| --- |
| license: cc-by-nc-sa-4.0 |
| pretty_name: EgoPressure |
| tags: |
| - hand-pose |
| - pressure-estimation |
| - egocentric-vision |
| - hand-object-interaction |
| - MANO |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| <h1 align="center">EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision</h1> |
|
|
| <div align="center"> |
|
|
| **CVPR 2025 (Highlight)** |
|
|
| [Yiming Zhao<sup>1*</sup>](https://yiming-zhao.github.io), [Taein Kwon<sup>1*</sup>](https://taeinkwon.com/), [Paul Streli<sup>1*</sup>](https://www.paulstreli.com), [Marc Pollefeys<sup>1,2</sup>](https://people.inf.ethz.ch/marc.pollefeys/), [Christian Holz<sup>1</sup>](https://www.christianholz.net/)<br/> |
|
|
| <sup>1</sup> ETH Zürich |
| <sup>2</sup> Microsoft<br/> |
| <sup>*</sup> Equal contribution<br/> |
| |
| </div> |
| |
| A dataset for **hand pressure and pose estimation in egocentric vision**. |
| Participants perform touch gestures on a |
| pressure-sensing touchpad (Sensel Morph) while being recorded by a |
| head-mounted egocentric camera and seven static Azure Kinects — providing |
| synchronized RGB-D from 8 views, fine-grained touch pressure for every |
| contact, and MANO hand mesh annotations. |
| |
| - 📄 Paper: https://arxiv.org/abs/2409.02224 |
| - 🛠️ Toolkit (download / load / visualize): https://github.com/eth-siplab/EgoPressure |
| - 🎬 Preview videos: https://drive.google.com/drive/folders/1JUIUvIR2jAV-ghYGtVLgBEN1JdCzkvnE |
| |
| <p align="center"> |
| <img src="assets/teaser.gif" width="800"> |
| </p> |
| |
| ## Composition |
| |
| | | | |
| |---|---| |
| | Participants | 21 (pseudonymized `p_001` … `p_021`) | |
| | Sequences | **1,344** — 32 gestures × 2 hands per participant | |
| | Gestures | palm/finger/index presses (high, low, no-contact), pinches, pinch-zoom, grasps, pull/push, drawing, typing + a calibration routine | |
| | Views | 8 synchronized RGB-D cameras (1 egocentric 1920×1080 + 7 static 2560×1440) at 30 Hz | |
| | Frames | ≈630K per camera stream (≈5M camera-frames overall) | |
| | Modalities | RGB, depth, hand masks, raw touch-pressure grid, UV pressure maps, MANO hand meshes (pose, shape, per-vertex refinement), per-frame egocentric camera pose | |
| |
| ## Quick start (toolkit) |
| |
| The official toolkit handles selective download, loading every modality, |
| geometry, and visualization: |
| |
| ```bash |
| pip install "egopressure[all] @ git+https://github.com/eth-siplab/EgoPressure" |
| |
| egopressure list |
| egopressure download --participants p_001 --cameras d --modalities rgb,depth,pressure,pose |
| egopressure video p_001 p_001_press_palm_low_x5_right --out seq.mp4 |
| ``` |
| |
| ```python |
| from egopressure import EgoPressure |
| |
| ep = EgoPressure.from_hub(participants=["p_001"], cameras=["d"], |
| modalities=["rgb", "depth", "pressure", "pose"]) |
| seq = ep.sequence("p_001", "p_001_press_palm_low_x5_right") |
| frame = seq.load_frame(60, depth=True) # rgb, depth, force, annotation |
| frame.show(camera="d", overlays=["mesh", "skeleton", "pressure"]) |
| ``` |
| |
| The toolkit's [data reference](https://github.com/eth-siplab/EgoPressure/blob/master/docs/DATA.md) |
| documents every field, unit, and convention. |
| |
| ## Download without the toolkit |
| |
| Everything is sharded by participant / sequence / camera / modality, so plain |
| `huggingface_hub` works too: |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download("eth-siplab/EgoPressure", repo_type="dataset", |
| local_dir="egopressure_data", |
| allow_patterns=["configs/p_001/*", |
| "data/p_001/*/cam-d.color.parquet", |
| "data/p_001/*/pressure.parquet", |
| "data/p_001/*/annotation.parquet"]) |
| ``` |
| |
| ### Layout |
|
|
| ``` |
| configs/<participant>/<sequence>.json # camera calibration + metadata |
| configs/<participant>/<sequence>_k4a/ # per-camera factory calibrations |
| data/<participant>/<sequence>/ |
| cam-d.color.parquet cam-1.color.parquet ... cam-7.color.parquet |
| cam-d.depth.parquet cam-1.depth.parquet ... cam-7.depth.parquet |
| cam-d.mask.parquet cam-1.mask.parquet ... cam-7.mask.parquet |
| pressure.parquet # Sensel grid + UV pressure |
| annotation.parquet # MANO pose/mesh + ego pose |
| ``` |
|
|
| Each Parquet shard holds one row per frame (`frame` column). |
|
|
| ### Modalities |
|
|
| | Shard | Content | |
| |---|---| |
| | `cam-*.color` | RGB frames as original JPEG bytes (ego `d`: 1920×1080; static `1..7`: 2560×1440; undistorted) | |
| | `cam-*.depth` | 512×512 uint16 depth in millimetres (PNG bytes), all 8 cameras; depth-sensor frame, covering the camera's color view (+32 px guard band) — `0` = no measurement | |
| | `cam-*.mask` | hand segmentation masks (PNG bytes) | |
| | `pressure` | raw Sensel Morph grid (105×185 float32, flattened) + normalised 224×224 UV pressure map with `[min, max]` range | |
| | `annotation` | MANO `vertices (778×3)`, `joint_position (21×3)`, `betas (10)`, `full_pose (48)`, `transl`, `normals`, `displacement`, per-static-camera `visible_vertices (7×778)`, per-frame egocentric camera pose (`ego_R` 3×3, `ego_T` 3) | |
|
|
| ### Geometry essentials |
|
|
| - World origin on the touchpad surface (240 × 137.5 mm active area, x–y plane |
| at z = 0); MANO vertices and the ego pose are in **metres**, static-camera |
| `ModelViewMatrix` extrinsics in **millimetres**. |
| - Images are undistorted → plain pinhole projection with the config intrinsics. |
| - Depth maps are in each camera's **depth-sensor frame** (not registered to |
| color) and cover the region co-visible with that camera's color image; |
| `configs/<p>/<seq>_k4a/` holds the factory calibrations — the toolkit's |
| `register_depth_to_color()` produces pixel-aligned RGB-D. |
| - Raw sensor counts convert to Newtons via `counts / 1736` (cell pitch 1.25 mm). |
|
|
| ## License & citation |
|
|
| Released under **CC BY-NC-SA 4.0** (non-commercial, academic). MANO-derived |
| annotations are additionally subject to the |
| [MANO license](https://mano.is.tue.mpg.de/license.html). |
|
|
| ```bibtex |
| @InProceedings{Zhao_2025_CVPR, |
| author = {Zhao, Yiming and Kwon, Taein and Streli, Paul and Pollefeys, Marc and Holz, Christian}, |
| title = {EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision}, |
| booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, |
| month = {June}, |
| year = {2025}, |
| pages = {27727--27738} |
| } |
| ``` |
|
|
