This is repository with inference code for paper "StylePeople: A Generative Model of Fullbody Human Avatars" (CVPR21). This code is for the part of the paper describing video-based avatars. For inference of generative neural textures model refer to this repository.

To use this repository you first need to download model checkpoints and some auxiliary files.

• Download the archive with data from Google Drive and unpack in into NeuralTextures/data/. It contains:
  • checkpoints for generative model and encoder network (data/checkpoint)
  • SMPL-X parameters for samples from AzurePeople dataset to run inference script on (data/smplx_dicts)
  • Some auxiliary data (data/uv_render and data/*.npy)
• Download SMPL-X models (SMPLX_{MALE,FEMALE,NEUTRAL}.pkl) from SMPL-X project page and move them to data/smplx/

The easiest way to build an environment for this repository is to use docker image. To build it, make the following steps:

1. Build the image with the following command:

bash docker/build.sh

2. Start a container:

bash docker/run.sh

It mounts root directory of the host system to /mounted/ inside docker and sets cloned repository path as a starting directory.

3. Inside the container install minimal_pytorch_rasterizer. (Unfortunately, docker fails to install it during image building)

pip install git+https://github.com/rmbashirov/minimal_pytorch_rasterizer

4. (Optional) You can then commit changes to the image so that you don't need to install minimal_pytorch_rasterizer for every new container. See docker documentation.

For now the only scenario in this repository involves rendering an image of a person from AzurePeople dataset with giver SMPL-X parameters.

Example:

python render_azure_person.py --person_id=04 --smplx_dict_path=data/smplx_dicts/04.pkl --out_path=data/results/

will render a person with id='04' with SMPL-X parameters from data/smplx_dicts/04.pkl and save resulting images to data/results/04.

For ids of all 56 people consult this table