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StylePeople

<img align=center src=./assets/icons/project.png width='32'/> Project   <img align=center src=./assets/icons/paper.png width='24'/> Paper  

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 generative neural textures model. For inference of video-based avatars refer to this repository.

Getting started

Data

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 StylePeople/data/. It contains:
  • checkpoints for generative model and encoder network (data/checkpoint)
  • A few samples from AzurePeople dataset to run inference script on (data/inference_samples)
  • A sample of smplx parameters (data/smplx_sample.pkl)
  • Some auxiliary data (data/uv_render and data/*.{yaml,pth,npy})
• Download SMPL-X models (SMPLX_{MALE,FEMALE,NEUTRAL}.pkl) from SMPL-X project page and move them to data/smplx/

Docker

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.

Usage

This repository provides three scripts with three scenarios, as it is shown on the scheme below:

Below are brief descriptions and examples of usage for these scripts. Please see their argparse help message for more details.

Sample texture from generative model

python sample_new_textures.py samples a number of neural textures from generative model and saves them on disc.

Example:

python sample_new_textures.py --n_samples=10 --texture_batch_name='my_run'

will sample 10 neural textures and save them to data/textures/my_run

Infer a neural texture for a given set of images

infer_texture.py fits a neural texture to a given set of data samples. See data/inference_samples/ for samples' examples.

Example:

python infer_texture.py --input_path=data/inference_samples/azure_04 --texture_out_dir=data/textures/azure_04

will load all data samples from data/inference_samples/azure_04 and save inferred texture to data/textures/azure_04

Render an image of a person with given neural texture and smplx parameters

render_person.py generates an image of a person with given shape, pose, expression and neural texture.

Example:

python render_person.py --texture_path=data/textures/my_run/0000/texture.pth --smplx_dict_path=data/smplx_sample.pkl --save_dir=data/my_person

will render a person with neural texture from data/textures/my_run/0000/texture.pth and smplx parameters from data/smplx_sample.pkl and save generated images to data/my_person.