Hugging Face's logo

Datasets:
noitomrobotics
/
HiPHI-Hand

Languages:
English
Size:
10K<n<100K
Tags:
motion-capture
human-motion
humanoid-robotics
human-object-interaction
whole-body-motion
bvh
License:
Dataset card Files
xet
 Community
1
HiPHI-Hand / README.md
nrobotics's picture
nrobotics
Upload README.md
c9cbcd0 verified
preview code
|
Raw History Blame Contribute Delete
10.4 kB
---
pretty_name: HiPHI-Hand
viewer: false
language:
- en
license: other
license_name: modalitynet-open-research-license
license_link: LICENSE.md
extra_gated_heading: "Request Access to ModalityNet Dataset"
extra_gated_description: "Please complete the questionnaire below and confirm your agreement with the [ModalityNet Open Research License](https://modalitynet.com/Open-Research-License) to gain access. For commercial licensing, you can contact us at [info@modalitynet.com](mailto:info@modalitynet.com)."
extra_gated_prompt: "Enter an email address that you can access. Use a company, organization, or university email when possible. By submitting this form, you confirm that the information is accurate, agree to the dataset terms as well as receiving followups from our research team."
extra_gated_button_content: "Submit Request"
extra_gated_fields:
"First Name": text
"Last / Family Name": text
"Company / Institution": text
"Affiliation":
type: select
options:
- "Academia"
- "Industry"
- "Personal"
"Professional / Company Email": text
"Intended Use Case":
type: select
options:
- "Academic Research"
- "Commercial Evaluation"
- "Non-profit / Personal"
- "Other"
"Country": country
"I confirm that I agree with the ModalityNet Open Research License agreement": checkbox
tags:
- motion-capture
- human-motion
- humanoid-robotics
- human-object-interaction
- whole-body-motion
- bvh
- framenet
size_categories:
- 10K<n<100K
---
<div align="center">
<img src="https://noitomrobotics.com/wp-content/uploads/2026/11/HiPHI-Hand-logo.png" alt="ModalityNet Logo" width="900px">
</div>
# Hand Skeleton Dataset
Numbered stereo fisheye clips, each with two hand-skeleton annotations in the same IMU frame. Clips are grouped into five datasets. Each dataset has its own camera calibration, shared by every clip in that dataset.
Clip folders are `0001/`, `0002/`, and so on, and the numbering restarts inside each dataset. A clip number is only an index within that dataset. The same number in two datasets is not the same recording. Each clip has its own frame count. Inside one clip, both videos and both annotation files cover the same frames, index `0 … T−1`. Frame `t` in a CSV is video frame `t` of that clip.
## Directory Structure
```text
HiPhi-Hand/
├── README.md
├── visualize.py
├── dataset_01/ clips 0001–0208 (208 clips)
│ ├── calibration.json
│ └── data/
│ ├── 0001/
│ │ ├── video/
│ │ │ ├── rgb_left.mp4
│ │ │ └── rgb_right.mp4
│ │ ├── annotation/
│ │ │ ├── mediapipe21.csv
│ │ │ └── vicon25.csv
│ │ └── visualization/
│ │ └── stereo_overlay.mp4
│ ├── 0002/
│ └── …
├── dataset_02/ clips 0001–0156 (156 clips)
├── dataset_03/ clips 0001–0120 (120 clips)
├── dataset_04/ clips 0001–0206 (206 clips)
└── dataset_05/ clips 0001–0104 (104 clips)
```
`dataset_02` through `dataset_05` use the same layout as `dataset_01`: `calibration.json` next to `data/<clip>/`.
- `video/rgb_left.mp4`, `video/rgb_right.mp4`: hardware-synchronized fisheye views, 1360×1360, H.264, 30 fps.
- `annotation/mediapipe21.csv`: 21 MediaPipe hand joints per hand, IMU coordinates, meters.
- `annotation/vicon25.csv`: 25 Vicon hand-skeleton joints per hand, same IMU frame, plus a global orientation quaternion.
- `visualization/stereo_overlay.mp4`: left and right images side by side (2720×1360) with the MediaPipe21 skeleton drawn on.
- `calibration.json`: stereo fisheye calibration for the clips in that dataset. Use the file that sits beside `data/`, not a copy from another dataset.
- `visualize.py`: redraws the overlay from a clip's videos, CSVs, and that dataset's calibration.
## Coordinate Frame
Annotations and calibration use one frame, named `imu` in `calibration.json`:
| Axis | Direction |
|---|---|
| +x | wearer's right |
| +y | up |
| +z | backward |
Unit is meters. `Left` / `Right` are the wearer's hands, not the left and right image.
## Annotation CSVs
Both files are UTF-8 CSV. A clip is complete when every frame has both hands and every joint has a finite position. `visualize.py` rejects a clip that is missing rows.
Common columns:
| Column | Meaning |
|---|---|
| `frame_index` | Frame number inside this clip, `0 … T−1`. Equals that clip's video frame index. |
| `hand_index` | `0` left, `1` right. |
| `hand` | `Left` or `Right`. Must match `hand_index`. |
| `joint_index` | Joint number inside that hand. See the tables below. |
| `joint_name` | Joint name. Must match `joint_index`. |
| `x_imu_m`, `y_imu_m`, `z_imu_m` | Joint position in the IMU frame, meters. |
`mediapipe21.csv` has 42 rows per frame (2 hands × 21 joints). `vicon25.csv` has 50 rows per frame (2 hands × 25 joints) and four extra columns:
| Column | Meaning |
|---|---|
| `global_quat_x_imu` | Quaternion x |
| `global_quat_y_imu` | Quaternion y |
| `global_quat_z_imu` | Quaternion z |
| `global_quat_w_imu` | Quaternion w (scalar) |
These are unit quaternions, stored in `(x, y, z, w)` order. The column names describe a global orientation expressed in the IMU frame. The position columns are enough to draw or compare the skeleton; the overlay script does not use the quaternions.
Vicon joint `Hand` (index 1) is the same 3D point as MediaPipe `WRIST` (index 0).
### MediaPipe21 order
Same names for both hands. Skeleton links used by the overlay: wrist to each finger, then along the finger.
| Index | Name |
|---|---|
| 0 | `WRIST` |
| 1–4 | `THUMB_CMC`, `THUMB_MCP`, `THUMB_IP`, `THUMB_TIP` |
| 5–8 | `INDEX_FINGER_MCP`, `INDEX_FINGER_PIP`, `INDEX_FINGER_DIP`, `INDEX_FINGER_TIP` |
| 9–12 | `MIDDLE_FINGER_MCP`, `MIDDLE_FINGER_PIP`, `MIDDLE_FINGER_DIP`, `MIDDLE_FINGER_TIP` |
| 13–16 | `RING_FINGER_MCP`, `RING_FINGER_PIP`, `RING_FINGER_DIP`, `RING_FINGER_TIP` |
| 17–20 | `PINKY_MCP`, `PINKY_PIP`, `PINKY_DIP`, `PINKY_TIP` |
### Vicon25 order
`joint_name` is the side prefix plus the suffix below (`LeftHand`, `RightForeArm`, …). Overlay links: `0–1`, then each finger chain starting at `Hand` (index 1). Index continues through `HandIndex`; the other fingers start directly at `Hand`.
| Index | Suffix | Role |
|---|---|---|
| 0 | `ForeArm` | forearm |
| 1 | `Hand` | wrist |
| 2 | `HandIndex` | index metacarpal |
| 3–6 | `HandIndex1` … `HandIndex4` | index finger |
| 7–10 | `HandMiddle1` … `HandMiddle4` | middle finger |
| 11 | `HandRing` | ring metacarpal |
| 12–15 | `HandRing1` … `HandRing4` | ring finger |
| 16 | `HandPinky` | pinky metacarpal |
| 17–20 | `HandPinky1` … `HandPinky4` | pinky finger |
| 21–24 | `HandThumb1` … `HandThumb4` | thumb |
## calibration.json
Stereo fisheye calibration. `format_version` is `2.0-stereo`. The overlay script rejects any other version.
```text
calibration.json
├── format_version "2.0-stereo"
├── coordinate_frame IMU frame of the 3D points
├── transform_convention how to read the 4×4 matrices
├── projection which points are in front of the camera
└── cameras
├── left
└── right
```
`transform_convention`:
- Matrices are named `T_target_source`: `T_camera_imu` takes an IMU point into that camera.
- Vectors are column vectors.
- `p_camera = R_camera_imu @ p_imu + t_camera_imu`, where `R` is the top-left 3×3 of `T_camera_imu` and `t` is the top-right 3×1.
- `T_imu_camera` is the inverse of `T_camera_imu`. The script checks `T_camera_imu @ T_imu_camera = I`, that `R` is a rotation (`det(R) = 1`), and that both cameras are present.
`projection.front_condition` is `camera_z_m > 1e-6`. Points on or behind the camera are not drawn.
Each of `cameras.left` and `cameras.right` has:
| Field | Meaning |
|---|---|
| `image_size_px` | `[width, height]`, here `[1360, 1360]`. Must match the video. |
| `intrinsics.fx_fy_cx_cy` | `[fx, fy, cx, cy]` in pixels. |
| `intrinsics.K` | 3×3 pinhole matrix built from those four values. The script requires the two to agree. |
| `distortion.model` | `opencv_equidistant_fisheye` (OpenCV `cv2.fisheye`). |
| `distortion.coefficients` | `[k1, k2, k3, k4]`. |
| `extrinsics.T_camera_imu` | 4×4 IMU → this camera. |
| `extrinsics.T_imu_camera` | 4×4 this camera → IMU. |
Distortion is the OpenCV equidistant fisheye model. With normalized camera coordinates `(x, y) = (X/Z, Y/Z)`:
```text
r = hypot(x, y)
theta = atan(r)
theta_d = theta * (1 + k1*theta^2 + k2*theta^4 + k3*theta^6 + k4*theta^8)
scale = 1 if r is about 0, else theta_d / r
u = fx * x * scale + cx
v = fy * y * scale + cy
```
A point is visible when `Z > 1e-6` and `(u, v)` lies inside the image.
Minimal projection:
```python
import json
import numpy as np
calibration = json.load(open("dataset_01/calibration.json"))
camera = calibration["cameras"]["left"]
T = np.asarray(camera["extrinsics"]["T_camera_imu"])
point_imu = np.array([-0.10, -0.28, -0.48]) # one CSV row, meters
point_camera = T[:3, :3] @ point_imu + T[:3, 3]
```
Apply the fisheye model above to `point_camera` to get a pixel in `rgb_left.mp4`. Use `cameras.right` for the right video. The same `point_imu` is projected into both images; do not transform it from one camera to the other first.
## Visualization Script
Requirements:
```bash
pip install numpy opencv-python
```
Run from `HiPhi-Hand/`. For a clip at `dataset_XX/data/<clip>`, the script looks for `calibration.json` two directories above the clip, which is `dataset_XX/calibration.json`.
```bash
python visualize.py --clip dataset_01/data/0001 --output check.mp4
```
Replace `dataset_01/data/0001` with any other clip. Pass `--calibration` only when the calibration file is not in that default place.
Optional arguments:
```bash
--layer mediapipe21 # default
--layer vicon25
--layer both
--max-frames 30
```
The output is a side-by-side MPEG-4 Part 2 video at the source frame rate. The wearer's left hand is drawn cyan and the right hand orange. VLC and similar players can open it; some browser players cannot.