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## News
**24/Sep./2022**: We are pleased to announce that our total prize will be up to 4000 CHF (4000 USD) for our two challenges ([hand](https://codalab.lisn.upsaclay.fr/competitions/4822), [action](https://codalab.lisn.upsaclay.fr/competitions/4820)). <br>
**15/Aug./2022**: Our challenges are open here ([hand](https://codalab.lisn.upsaclay.fr/competitions/4822), [action](https://codalab.lisn.upsaclay.fr/competitions/4820)). Our 1st phase of the challenges will be closed at October 1st, 2022. <br>
**9/May/2022**: The download script (downlaod_script.py) is uploaded. <br>
**8/May/2022**: Labels for hand pose and object pose are released. Please check https://h2odataset.ethz.ch <br>
**4/May/2022**: The H2O dataset will be part of the workshop, Human Body, Hands, and Activities from Egocentric and Multi-view Cameras@ECCV2022. Please check our workshop site (https://sites.google.com/view/egocentric-hand-body-activity) and our challenges. <br>
**12/Mar./2022**: Visualization code is published. https://github.com/taeinkwon/h2oplayer <br>
**11/Mar./2022**: Updated new labels to the existing files. <br>
**8/Dec./2021**: Please Download new labels "manolabel_v1.1.tar.gz" in https://h2odataset.ethz.ch for MANO parameters. We will also update existing files soon. <br>
# Visualization code (H2OPlayer)
Please check the visualization code in the following link: https://github.com/taeinkwon/h2oplayer.
# Download script
## Dependencies
- python3
- tqdm
- pathlib
- requests
- argparse
Once you receive the username and password from the download page (https://h2odataset.ethz.ch), you can either download directly from the download page (https://h2odataset.ethz.ch) or use download_script.py.
```
python download_script.py --username "username" --password "password" --mode "type of view" --dest "dest folder path"
```
We provide three different modes (views):
- all: download the entire data, including five views (four fixed views, one egocentric view).
- ego: download only egocentric-view data.
- pose: download only pose (hand, object, egocentric view) without RGB-D images.
# Dataset Structure
<pre>
.
├── h1
│ ├── 0
│ │ │── cam0
│ │ │ ├── rgb
│ │ │ ├── depth
│ │ │ ├── cam_pose
│ │ │ ├── hand_pose
│ │ │ ├── hand_pose_MANO
│ │ │ ├── obj_pose
│ │ │ ├── obj_pose_RT
│ │ │ ├── action_label (only in cam4)
│ │ │ ├── rgb256 (only in cam4)
│ │ │ ├── verb_label
│ │ │ └── cam_intrinsics.txt
│ │ ├── cam1
│ │ ├── cam2
│ │ ├── cam3
│ │ └── cam4
│ ├── 1
│ ├── 2
│ ├── 3
│ └── ...
├── h2
├── k1
├── k2
└── ...
</pre>
cam0 ~ cam3 are fixed cameras. cam4 is an head-mounted camera (egocentric view). <br>
train_sequences = ['subject1/h1', 'subject1/h2', 'subject1/k1', 'subject1/k2', 'subject1/o1', 'subject1/o2', 'subject2/h1', 'subject2/h2', 'subject2/k1',
'subject2/k2', 'subject2/o1', 'subject2/o2', 'subject3/h1', 'subject3/h2', 'subject3/k1'] (subject 1,2,3) <br>
val_sequences = ['subject3/k2', 'subject3/o1', 'subject3/o2'] (subject 3)<br>
test_sequences = ['subject4/h1', 'subject4/h2', 'subject4/k1', 'subject4/k2', 'subject4/o1', 'subject4/o2'] (subject4)<br>
### rgb
1280 * 720 resolution rgb images
### rgb256
455 * 256 resolution resized rgb images
### depth
1280 * 720 resolution depth images
### cam_pose
cam_to_world rotion matrix <br>
16 numbers, 4x4 camera matrix
### hand_pose
cam_to_hand <br>
1 (whether annotate or not, 0: not annotate 1: annotate) + 21 * 3 (x, y, z in order) + 1 + 21 * 3 (right hand) <br>
First 64 numbers belong to the left hand. Next 64 numbers belong to the right hand
### hand_pose_MANO
1 (whether annotate or not, 0: not annotate 1: annotate) + 3 translation values + 48 pose values + 10 shape values + 1 + 3 + 48 + 10 (right hand) <br>
First 59 numbers belong to the left hand. Next 59 numbers belong to the right hand
### obj_pose
cam_to_obj <br>
1 (object class) + 21 * 3 (x, y, z in order) <br>
21 numbers : 1 center, 8 corners, 12 mid edge point. <br>
0 background (no object) <br>
1 book <br>
2 espresso <br>
3 lotion <br>
4 spray <br>
5 milk <br>
6 cocoa <br>
7 chips <br>
8 capuccino <br>
<img id="image_canv" src="obj_point_order.jpg"/>
### object_pose_RT
1 (object class) + 16 numbers, 4x4 camera matrix
### verb_label
0 background (no verb) <br>
1 grab <br>
2 place <br>
3 open <br>
4 close <br>
5 pour <br>
6 take out <br>
7 put in <br>
8 apply <br>
9 read <br>
10 spray <br>
11 squeeze <br>
### action_label
Combination of a noun (object class) and a verb (verb label)
0 background <br>
1 grab book <br>
2 grab espresso <br>
3 grab lotion <br>
4 grab spray <br>
5 grab milk <br>
6 grab cocoa <br>
7 grab chips <br>
8 grab cappuccino <br>
9 place book <br>
10 place espresso <br>
11 place lotion <br>
12 place spray <br>
13 place milk <br>
14 place cocoa <br>
15 place chips <br>
16 place cappuccino <br>
17 open lotion <br>
18 open milk <br>
19 open chips <br>
20 close lotion <br>
21 close milk <br>
22 close chips <br>
23 pour milk <br>
24 take out espresso <br>
25 take out cocoa <br>
26 take out chips <br>
27 take out cappuccino <br>
28 put in espresso <br>
29 put in cocoa <br>
30 put in cappuccino <br>
31 apply lotion <br>
32 apply spray <br>
33 read book <br>
34 read espresso <br>
35 spray spray <br>
36 squeeze lotion <br>
## For Actions
[Train set file](action_labels/action_train.txt) <br>
[Validation set file](action_labels/action_val.txt) <br>
[Test set file](action_labels/action_test.txt) <br>
## For Poses
[Train set file](pose_lists/pose_test.txt) <br>
[Validation set file](pose_lists/pose_train.txt) <br>
[Test set file](pose_lists/pose_test.txt) <br>
### cam_instrinsics.txt
six numbers : fx, fy, cx, cy, width, height
# Citations
If you find any usefulness in this dataset, please consider citing:
```
@InProceedings{Kwon_2021_ICCV,
author = {Kwon, Taein and Tekin, Bugra and St\"uhmer, Jan and Bogo, Federica and Pollefeys, Marc},
title = {H2O: Two Hands Manipulating Objects for First Person Interaction Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {10138-10148}
}
```
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