DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image

This is an official repository for the paper

DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
Tetiana Martyniuk, Orest Kupyn, Yana Kurlyak, Igor Krashenyi, Jiři Matas, Viktoriia Sharmanska
CVPR 2022

Installation

The code uses Python 3.6.

Create a Conda virtual environment:

conda create --name DAD-3DHeads python=3.6
conda activate DAD-3DHeads

Clone the project and install requirements:

git clone https://github.com/PinataFarms/DAD-3DHeads.git
cd DAD-3DHeads

pip install -r requirements.txt

Training

Prepare the DAD-3DHeads dataset:

First, you need to download the DAD-3DHeads dataset and extract it to the dataset/DAD-3DHeadsDataset directory. The dataset is available upon request. Please fill in this form to get access to it.

The dataset directory structure should be the following:

./dataset
--DAD-3DHeadsDataset
----train
------images
--------<ID>.png
------annotations
--------<ID>.json
------train.json
----val
------images/<ID>.png
------annotations/<ID>.json
------val.json
----test
------images/<ID>.png
------test.json

Annotations <ID>.json file structure:

--vertices
--model_view_matrix
--projection_matrix

Metadata [train|val|test].json file structure:

--item_id
--annotation_path
--img_path
--bbox #[x, y, w, h] format
----0
----1
----2
----3
--attributes
----quality #[hq, lq]
----gender #[female, male, undefined]
----expression #[true, false]
----age #[child, young, middle_aged, senior]
----occlusions #[true, false]
----pose #[front, sided, atypical]
----standard light #[true, false]

The training code uses hydra. To change the training setup, add a new or edit the existing .yaml file in the model_training/config folder.

Visualize the ground-truth labels:

python visualize.py <subset> <id>

Pick subset from the train, val, test options, and the corresponding item_id (without file extension).

Run training code:

python train.py

Demo

First row (from left to right): input image, 68 2D face landmarks visualization, 191 2D face landmarks visualization, 445 2D face landmarks visualization.
Second row (from left to right): face mesh visualization, head mesh visualization, head pose visualization, 3D head mesh.

Run demo:

python demo.py <path/to/input/image.png> <path/to/output/folder> <type_of_output>

# Visualize 68 2D face landmarks
python demo.py images/demo_heads/1.jpeg outputs 68_landmarks

# Visualize 191 2D face landmarks
python demo.py images/demo_heads/1.jpeg outputs 191_landmarks

# Visualize 445 2D face landmarks
python demo.py images/demo_heads/1.jpeg outputs 445_landmarks

# Visualize face mesh
python demo.py images/demo_heads/1.jpeg outputs face_mesh

# Visualize head mesh
python demo.py images/demo_heads/1.jpeg outputs head_mesh

# Visualize head pose
python demo.py images/demo_heads/1.jpeg outputs pose

# Get 3D mesh .obj file
python demo.py images/demo_heads/1.jpeg outputs 3d_mesh

# Get flame parameters .json file
python demo.py images/demo_heads/1.jpeg outputs flame_params

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation

If you use the DAD-3DHeads Dataset and/or this code - implicitly or explicitly - for your research projects, please cite the following paper:

@inproceedings{dad3dheads,
    title={DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image},
    author={Martyniuk, Tetiana and Kupyn, Orest and Kurlyak, Yana and Krashenyi, Igor and Matas, Ji\v{r}i and Sharmanska, Viktoriia},
    booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
    year={2022}
}