Awesome

News

• 25/01/2022 To make the comparison with our Neural Human Performer easier, we provide the evaluation results (images, summary) at here.
• 25/01/2022 The code and the pretrained model for the Neural Human Performer are now released!

Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering

Project Page | Video | Paper

Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
Youngjoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs
NeurIPS 2021 (Spotlight)

Installation

Please see INSTALL.md. We provide the pretrained models at here

Run the code on ZJU-MoCap

Please see INSTALL.md to download the dataset.

The provided commands are for the evaluation and visualization on the unseen subjects (subject 387, 393, 394) with three input cameras (Camera B1, B8, B16). To make the comparison with our Neural Human Performer easier, we provide the evaluation results (images, summary) at here.

• To train / test on the different subjects, please modify the lib/datasets/get_human_info.py file.
• To use different input views, please modify the test_input_view command argument.
• To test on the different setting (e.g., test on unseen poses of seen subjects), please modify the test_mode command argument accordingly. There are four different settings: model_o_motion_o model_o_motion_x model_x_motion_o model_x_motion_x

Evaluation on ZJU-MoCap

1. Download the pretrained model and put it to $ROOT/data/trained_model/if_nerf/demo/latest.pth.
2. Quantitative evaluation:

   CUDA_VISIBLE_DEVICES=0 python run.py --type evaluate --cfg_file configs/train_or_eval.yaml virt_data_root data/zju_mocap rasterize_root data/zju_rasterization ratio 0.5 H 1024 W 1024 test_input_view "0,7,15" run_mode test test_mode model_x_motion_x exp_name demo resume True test_sample_cam True test.epoch -1 exp_folder_name debug gpus "0,"
   

3. The results (images, summary) will be saved at $ROOT/data/result/if_nerf/{$exp_name}/epoch_{$test.epoch}/{$exp_folder_name}

Visualization on ZJU-MoCap

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/if_nerf/demo/latest.pth.

2. Visualization:

   • Free-viewpoint rendering

   # render frames
   # results will be saved at '$ROOT/data/perform/{$exp_name}/epoch_{$test.epoch}/{$exp_folder_name}'
   CUDA_VISIBLE_DEVICES=0 python run.py --type visualize --cfg_file configs/performance.yaml virt_data_root data/zju_mocap rasterize_root data/zju_rasterization ratio 0.5 H 1024 W 1024 test_input_view "0,7,15" test_mode model_x_motion_x exp_name demo resume True test.epoch -1 exp_folder_name debug gpus "0,"
   
   # generate video
   python gen_freeview_video.py 
   

   <p float="left"> <img src="https://github.com/YoungJoongUNC/Neural_Human_Performer/blob/main/image/freeview_subject_0.gif?raw=true" width="30%" height="30%" /> <img src="https://github.com/YoungJoongUNC/Neural_Human_Performer/blob/main/image/freeview_subject_1.gif?raw=true" width="30%" height="30%" /> <img src="https://github.com/YoungJoongUNC/Neural_Human_Performer/blob/main/image/freeview_subject_2.gif?raw=true" width="30%" height="30%" /> </p>
   • Mesh recosntruction

   # reconstruct the mesh
   # results will be saved at '$ROOT/data/mesh/{$exp_name}/epoch_{$test.epoch}/{$exp_folder_name}'
   CUDA_VISIBLE_DEVICES=0 python run.py --type visualize --cfg_file configs/reconstruction.yaml virt_data_root data/zju_mocap rasterize_root data/zju_rasterization ratio 0.5 H 1024 W 1024 test_input_view "0, 7, 15" test_mode model_x_motion_x exp_name demo resume True test.epoch -1 exp_folder_name debug gpus "1,"
   
   # render mesh
   # render mesh from 5th frame of 0th human
   export MESA_GL_VERSION_OVERRIDE=3.3
   python tools/render_mesh.py --exp_name demo --epoch -1 --exp_folder_name debug --dataset zju_mocap --human_idx 0 --frame_idx 5
   

Training on ZJU-MoCap

1. Train:

   # training
   CUDA_VISIBLE_DEVICES=0 python train_net.py --cfg_file configs/train_or_eval.yaml virt_data_root data/zju_mocap rasterize_root data/zju_rasterization ratio 0.5 H 1024 W 1024 run_mode train jitter True exp_name nhp resume True gpus "0,"
   

2. Tensorboard:

   tensorboard --logdir data/record/if_nerf
   

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@article{kwon2021neural,
  title={Neural human performer: Learning generalizable radiance fields for human performance rendering},
  author={Kwon, Youngjoong and Kim, Dahun and Ceylan, Duygu and Fuchs, Henry},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

Acknowledgments

We thank Sida Peng of Zhejiang University, Hangzhou, China, for very many helpful discussions on a variety of implementation details of Neural Body. We thank Ruilong li and Alex Yu of UC Berkeley for many discussions on the AIST++ dataset and pixelNeRF details. We thank Prof. Alex Berg of UNC for the generous offer of computational resources and Misha Shvets of UNC for a useful tutorial on it. This work was partially supported by National Science Foundation Award 1840131.

Contact

For questions, please contact youngjoong@cs.unc.edu.