Accompanying PyTorch code for the paper "3D Human Pose Estimation with 2D Marginal Heatmaps".

Requirements:

• Linux
• Docker
• Docker Compose
• NVIDIA Container Toolkit (for GPU support)

1. Copy docker-compose.yml.example to docker-compose.yml.

2. At this stage docker-compose.yml will contain example volume mounts for the datasets. You will need to edit the entries for datasets that you have prepared, and remove the others.

   For example, if you wish to use the MPI-INF-3DHP dataset, you must replace /host/path/to/mpi3d with the actual path to the prepared MPI-INF-3DHP data on your computer.

You only need to prepare the datasets that you are interested in using.

1. Use the scripts available at https://github.com/anibali/h36m-fetch to download and preprocess Human3.6M data.
2. Edit the volume mounts in docker-compose.yml so that the absolute location of the processed/ directory created by h36m-fetch is bound to /datasets/h36m inside the Docker container.

1. Download the original MPI-INF-3DHP dataset.
2. Use the src/margipose/bin/preprocess_mpi3d.py script to preprocess the data.
3. Edit the volume mounts in docker-compose.yml so that the absolute location of the processed MPI-INF-3DHP data is bound to /datasets/mpi3d inside the Docker container.

1. Edit the volume mounts in docker-compose.yml so that the desired installation directory for the MPII Human Pose dataset is bound to /datasets/mpii inside the Docker container.
2. Run the following to download and install the MPII Human Pose dataset:

   $ ./run.sh bash
   $ chmod 777 -R /datasets/mpii
   $ python
   >>> from torchdata import mpii
   >>> mpii.install_mpii_dataset('/datasets/mpii')
   

Showoff is a display server which allows you to visualise model training progression. The following steps guide you through starting a Showoff server and configuring MargiPose to use it.

1. Change POSTGRES_PASSWORD in showoff/postgres.env. Using a randomly generated password is recommended.
2. Change COOKIE_SECRET in showoff/showoff.env. Once again, using a randomly generated value is recommended.
3. From a terminal in the showoff directory, run docker-compose up -d showoff. This will start the Showoff server.
4. Open localhost:13000 in your web browser.
5. Log in using the username "admin" and the password "password".
6. Change the admin password.
7. Open up showoff/showoff-client.env in a text editor.
8. From the Showoff account page, add a new API key. Copy the API key ID and secret key into showoff-client.env (you will need to uncomment the appropriate lines).

A run.sh launcher script is provided, which will run any command within a Docker container containing all of MargiPose's dependencies. Here are a few examples.

Train a MargiPose model on the MPI-INF-3DHP dataset:

./run.sh margipose train with margipose_model mpi3d

Train without pixel-wise loss term:

./run.sh margipose train with margipose_model mpi3d "model_desc={'settings': {'pixelwise_loss': None}}"

Evaluate a model's test set performance using the second GPU:

./run.sh margipose --device=cuda:1 eval --model margipose-mpi3d.pth --dataset mpi3d-test

Explore qualitative results with a GUI:

./run.sh margipose gui --model margipose-mpi3d.pth --dataset mpi3d-test

Run the project unit tests:

./run.sh pytest

Pretrained models are available for download:

• margipose-mpi3d.pth [221.6 MB]
  • Trained on MPI-INF-3DHP and MPII examples
• margipose-h36m.pth [221.6 MB]
  • Trained on Human3.6M and MPII examples

You can try out the pretrained model like so:

./run.sh margipose infer --model margipose-mpi3d.pth --image resources/man_running.jpg

(C) 2018 Aiden Nibali

This project is open source under the terms of the Apache License 2.0.

If you use any part of this work in a research project, please cite the following paper:

@article{nibali2018margipose,
  title={3D Human Pose Estimation with 2D Marginal Heatmaps},
  author={Nibali, Aiden and He, Zhen and Morgan, Stuart and Prendergast, Luke},
  journal={arXiv preprint arXiv:1806.01484},
  year={2018}
}