Awesome
Adversarial Parametric Pose Prior
This is a Pytorch implementation of the CVPR'22 paper "Adversarial Parametric Pose Prior".
You can find the paper here.
<p align="center"> <img src="demos/demo.gif" alt="animated" width=80%/></p> <h4 align="center">Examples of spherical interpolations when sampling from GAN-S pose prior</h4>Installation
We provide the conda environment for Linux. To create and activate it, do:
conda env create -f environment.yaml
conda activate adv_prior
The main requirements are:
- python=3.8, numpy, matplotlib
- scipy, scikit-learn, jupyter
- easydict, pyyaml, tqdm
- pytorch=1.7, cudatoolkit=10.2
- pytorch3d
- smplx
- torchgeometry
As for SMPL mesh, put the basicModel_neutral_lbs_10_207_0_v1.0.0.pkl pickle file from SMPLIFY_CODE_V2.ZIP in data/. Rename the file, replacing basicModel with smpl.
Technical Details
Note: We make use of the torchgeometry library which contains an error. For more details please see this StackOverflow thread. Essentially, the file {ANACONDA_HOME}/envs/adv_prior/lib/python3.8/site-packages/torchgeometry/core/conversions.py must be updated
in the function rotation_matrix_to_quaternion:
- line 302:
mask_c1 = mask_d2 * ~(mask_d0_d1)mask_d2 * (1 - mask_d0_d1) - line 303:
mask_c2 = ~(mask_d2) * mask_d0_nd1(1 - mask_d2) * mask_d0_nd1 - line 304:
mask_c3 = (~(mask_d2)) * (~(mask_d0_nd1))(1 - mask_d2) * (1 - mask_d0_nd1)
Training
To launch the training of the GAN-S model on AMASS data, run:
python run/main.py --cfg experiments/train_gan.yaml
Demos
We provide two short demo jupyter notebooks for sampling from and interpolating in the latent space. All details can be found here.