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GTA-Human

This is the first version of our datasets, which are built upon GTA-V for human pose and shape estimation. It features single-person color image sequences with SMPL annotations. Models pretrained on GTA-Human can be found at MMHuman3D.

Downloads

A small sample can be downloaded from here. To download the full dataset, please see below.

Option 1: OpenXLab

GTA-Human is currently hosted on OpenXLab. We recommend download files using CLI tools:

openxlab dataset download --dataset-repo OpenXDLab/GTA-Human --source-path /gta-human_release --target-path /home/user/

You can selectively download files that you need, for example:

openxlab dataset download --dataset-repo OpenXDLab/GTA-Human --source-path /gta-human_release/images000.zip --target-path /home/user/gta-human_release/

Option 2: OneDrive

We have backed-up all files on OneDrive.

Data Structure

Please download the .zip files and place in the same directory.

gta-human_release/   
├── image000.zip
├── image001.zip
├── ...
├── image030.zip
└── annotations.zip

Then decompress them:

unzip "*.zip"

The file structure should look like this:

gta-human_release/   
├── images/  
│   └── seq_xxxxxxx/
│       ├── 00000000.jpeg
│       ├── 00000001.jpeg
│       └── ...
│
└── annotations/ 
        ├── seq_xxxxxx.pkl
        └── ...

Data Loading

To read the images:

import cv2
color_bgr = cv2.imread('/path/to/xxxxxxx.jpeg')
color_rgb = cv2.cvtColor(color_bgr, cv2.COLOR_BGR2RGB)  # if RGB images are used

To read the annotations:

import numpy as np
annot = dict(np.load('/path/to/xxxxxxx.pkl', allow_pickle=True))

Each .pkl consists of the following:

{
    'is_male': bool,
    'fov': float, 
    'keypoints_2d': np.array of shape (n, 100, 3), 
    'keypoints_3d': np.array of shape (n, 100, 4),
    'occ': np.array of shape (n, 100),
    'self_occ': np.array of shape (n, 100),
    'num_frames': int,
    'weather': str, 
    'daytime': tuple, 
    'location_tag': str,
    'betas': np.array of shape (n, 10),
    'body_pose': np.array of shape (n, 69),
    'global_orient': np.array of shape (n, 3),
    'transl': np.array of shape (n, 3),
    'bbox_xywh': np.array of shape (n, 4),
}

Notes:

  • fov has a constant value of 50.0.
  • keypoints
    • keypoints_3d are 3D keypoints provided by the games' API, format is (x, y, z, 1.0).
    • keypoints_2d are projeced 3D keypoints on the image plane, format is (u, v, 1.0).
    • Definition of the 100 keypoints can be found in MMHuman3D.
  • occ indicates if a keypoint is occluded.
  • self_occ indicates of a keypoint is occluded by the person's own body parts.
  • daytime uses a (hour, minute, second) convention.

Visualization

We provide a pyrender-based visualization tool to overlay 3D SMPL annotations on 2D images. A small sample can be downloaded from here.

python visualizer.py <--root_dir> <--seq_name> <--body_model_path> <--save_path>
  • root_dir (str): root directory in which data is stored.
  • seq_name (str): sequence name, in the format 'seq_xxxxxxxx'.
  • body_model_path (str): directory in which SMPL body models are stored.
  • save_path (str): path to save the visualization video.

Example:

python visualizer.py --root_dir /home/user/gta-human_release --seq_name seq_00000012 --body_model_path /home/user/body_models/ --save_path /home/user/seq_00000012_visual.mp4

Note that the SMPL model path should consist the following structure:

body_models/   
└── smpl/  
    └── SMPL_NEUTRAL.npz

The body models may be downloaded from the official website.