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Cinematic Transfer

The repo is the official implemenation of Cinematic Behavior Transfer via NeRF-based Differentiable Filming.

[Project Homepage] [arxiv]

We have VirtualFilmStudio to showcase some of our explorations in filmmaking directions.

Getting start

git clone https://github.com/VirtualFilmStudio/Cinetransfer.git

1 SMPL Visualization

We recommend you use anaconda to run our CinematicTransfer.

1.1 Install SLAHMR

Please refer to SLAHMR work to install env. Notes:

1.2 Preprocess data

Prepare test data. Please place data according to the following framework.

|-Cinetransfer
| |-camera_optim
| |-data
| |-torch_ngp
| |-slahmr
| | |-demo
| | | |-videos
| | | | |-arc2.mp4 
|...

Preprocess data by using SLAHMR.

cd {YOUR_ROOT}/Cinetransfer/slahmr/slahmr

# delete slahmr default test dataset
rm ../demo/videos/022691_mpii_test.mp4

# copy test data to slahmr demo
cp ../../data/videos/arc2.mp4 ../demo/videos/

# preprocess data by using SLAHMR
# If the server is not configured with virtual graphics, it is recommended to set 'run_vis=False'
source activate slahmr && python run_opt.py data=video data.seq=arc2 data.root={YOUR_ROOT}/Cinetransfer/slahmr/demo run_opt=True run_vis=True

After execution, an outputs folder will be generated. You can find a folder starting with arc2, which contains optimized smpl.

2 Prepare camera optimization data

mkdir {YOUR_ROOT}/Cinetransfer/data/arc2

cd {YOUR_ROOT}/Cinetransfer/camera_optim

# Copy the slahmr preprocessed data to the Cinetransfer/data folder and adjust the data structure
source activate slahmr && python trans_slahmr_to_nerf.py --src_root {YOUR_ROOT}/Cinetransfer/slahmr/demo --dst_root {YOUR_ROOT}/Cinetransfer/data

# Generate mask data
source activate slahmr && python filmingnerf/preproc/gen_mask.py --data data --seq_name arc2 --data_path {YOUR_ROOT}/Cinetransfer

Copy {YOUR_ROOT}/Cinetransfer/slahmr/outputs/.../arc2-xxxx/motion_chunks/arc2_{MAX_NUM}_world_results.npz to the {YOUR_ROOT}/Cinetransfer/data/arc2/ folder and rename it to arc2.npz.

3 Optimize camera

3.1 Install torch-ngp and related env

conda deactivate
cd {YOUR_ROOT}/Cinetransfer
git clone --recursive https://github.com/ashawkey/torch-ngp.git
mv torch-ngp torch_ngp
cd torch_ngp

conda create -n cinetrans python=3.9 -y
source activate cinetrans && pip install -r requirements.txt

source activate cinetrans && conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia

# install the tcnn backbone
source activate cinetrans && pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

# install all extension modules
bash scripts/install_ext.sh

cd raymarching
source activate cinetrans && python setup.py build_ext --inplace
# install to python path (you still need the raymarching/ folder, since this only install the built extension.)
source activate cinetrans && pip install .

Notes:

  • If you have any problems installing torch-ngp environment .You can refer to torch-ngp.
  • Rename {YOUR_ROOT}/Cinetransfer/torch-ngp to {YOUR_ROOT}/Cinetransfer/torch_ngp.

3.2 Training d-nerf

cd {YOUR_ROOT}/Cinetransfer/camera_optim
source activate cinetrans && sudo apt-get update 
source activate cinetrans && sudo apt install xvfb

source activate cinetrans && pip install einops chardet
source activate cinetrans && pip install pyrender
source activate cinetrans && pip install smplx[all]
source activate cinetrans && pip install imageio[ffmpeg] imageio[pyav]

Open {YOUR_ROOT}/Cinetransfer/torch_ngp/dnerf/utils.py, in the file top add this

import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
source activate cinetrans && xvfb-run --auto-servernum --server-num=1 python run_train_nerf.py --seq_name arc2

3.3 Check first frame pose

By using camera_ui to optim first_frame.

cd {YOUR_ROOT}/Cinetransfer/camera_optim
source activate cinetrans && pip install gradio
source activate cinetrans && pip install plotly

# temp visual result can be found in {out_dir}/{seq_name}/cache_first_pose.png
source activate cinetrans && python run_camera_ui.py  --seq_name arc2 --out_dir ../data/cam_optim_res

When open the UI, please follow those steps to check first pose:

  • Step1. Adjust the slide rail on the left to control the Camera pose.
  • Step2. Click the 'render' button to view the rendering results.
  • Step3. Finally click the 'optim' button to optimize a single frame.

Note: You can repeat the above process until you select the most suitable camera pose.

3.4 Optim camera trajectory

cd {YOUR_ROOT}/Cinetransfer/camera_optim

source activate cinetrans && python run_camera_opt.py --seq_name arc2 --out_dir ../data/cam_optim_res

3.5 Visualization

cd {YOUR_ROOT}/Cinetransfer/camera_optim
# smooth (optional)
source activate cinetrans && xvfb-run --auto-servernum --server-num=1 python run_camera_smooth.py --seq_name arc2 --out_dir ../data/cam_optim_res

source activate cinetrans && xvfb-run --auto-servernum --server-num=1 python run_camera_vis.py --seq_name arc2 --out_dir ../data/cam_optim_res

arc2

Citation

If you find this paper and repo useful for your research, please consider citing our paper.

@article{jiang2023cinematic,
      title={Cinematic Behavior Transfer via NeRF-based Differentiable Filming},
      author={Jiang, Xuekun and Rao, Anyi and Wang, Jingbo and Lin, Dahua and Dai, Bo},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      year={2024}
  }

Acknowledgements

The code is based on SLAHMR, torch-ngp, thanks to them!