Official implementation for the paper Generative Neural Articulated Radiance Fields in NeurIPS 2022.
NOTE: This repository only contains scripts for training, evaluating, and visualizing human body models. Human face generation code / checkpoint and body datasets (AIST++, SHHQ, DeepFashion) can be provided upon request.
Contact: [email protected]
train.py: Script used to train GNARF model.
visualizer.py: Script for animating and visualizing a trained GNARF model. [CURRENTLY UNTESTED]
generate_video.py: Script to animate a generated result according to a pose file.
calc_metrics.py: Script to compute metrics for a specific model checkpoint.
Pre-trained GNARF models can be downloaded here
A GNARF model for a specific dataset can be trained as follows:
CUDA_VISIBLE_DEVICES=0,1,2,3,5,6,7,8 python train.py --data=/path/to/dataset --cfg=[shhq|aist_rescaled|deepfashion] --gpus=8 --batch=32 --gamma=5 --aug=noaug --outdir=./results --projector surface_field --warping_mask mesh --disc_bodypose_cond=True --neural_rendering_resolution_final=120
where the correct cfg is chosen based on the dataset being used.
To generate results from a specific checkpoint, driven by poses in a .npy file (for example those included in the uploaded data):
python generate_video.py --network results/network-snapshot-******.pkl --pose_data path/to/params/params.npy --output_dir output_vids
To evaluate a specific checkpoint for various metrics, such as FID, use the following command:
CUDA_VISIBLE_DEVICES=0 python calc_metrics.py results/network-snapshot-******.pkl --metrics fid50k_full --data /path/to/dataset --gpus 1
If find our work useful in your research, please cite:
@inproceedings{bergman2022gnarf,
author = {Bergman, Alexander W. and Kellnhofer, Petr and Yifan, Wang and Chan, Eric R., and Lindell, David B. and Wetzstein, Gordon},
title = {Generative Neural Articulated Radiance Fields},
booktitle = {NeurIPS},
year = {2022},
}