HDNet: Human Depth Estimation For Multi-Person Camera-Space Localization

This repository includes the source code for our ECCV 2020 paper on monocular image human depth estimation for camera-space multi-person 3D pose localization. Please read our paper for more details at https://arxiv.org/abs/2007.08943.

Bibtex:

@inproceedings{lin2020hdnet,
    title={HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization},
    author={Lin, Jiahao and Lee, Gim Hee},
    booktitle={ECCV},
    year={2020}
}

Environment

The code is developed and tested on the following environment

Python 3.5.2
PyTorch 1.1.0
CUDA 9.0

Dataset

The source code is for training and testing on Human3.6M dataset.

Training data

To generate images for training, we provide a script generate_training_images.py to transform raw videos to full resolution images with proper naming. Please download raw videos from Human3.6M website. After running the provided script, put the generated train folder under data/Human36M/images/ directory.

Testing data

Testing images can be downloaded here. Put the test folder under data/Human36M/images/ directory.

Annotations

Processed annotations can be downloaded here. Put all files under data/Human36M/annotations/ directory.

The dataset should be organized as shown below:

    data/Human36M
            └── images
                └── train
                    ├── s_01_act_02_subact_01_ca_01/
                    ├── ...
                    └── s_08_act_16_subact_02_ca_04/
                └── test
                    ├── s_09_act_02_subact_01_ca_01/
                    ├── ...
                    └── s_11_act_16_subact_02_ca_04/
            └── annotations
                ├── subject_1.h5
                ├── subject_1_cpn.h5
                ├── ...
                ├── subject_11.h5
                └── subject_11_cpn.h5

Training

To train a model, run:

python train.py [OPTIONS value]

Refer to the source code in train.py for available OPTIONS.

Logs and models will be saved under exp/[EXP_TAG].

Evaluation

To evaluate a model named EXP_TAG, run:

python test.py --tag EXP_TAG

Our pre-trained model is available for download here. Place the model under exp/pretrained/saved_models/ and run:

python test.py --tag pretrained

jiahaoLjh / HumanDepth