JAWS: Just a Wild Shot for Cinematic Transfer in Neural Radiance Fields

By Xi Wang*, Robin Courant*, Jinglei Shi, Eric Marchand and Marc Christie

CVPR 2023

Project Page | arXiv | Paper + Supp

Installation

1. Create working environment:

conda create --name jaws -y python=3.10
conda activate jaws

2. Install dependencies (adapt it according to your CUDA version):

conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

3. Use the correct torch-ngp version:

mkdir ./lib
git clone git@github.com:ashawkey/torch-ngp.git
mv torch-ngp torch_ngp
cd torch_ngp
git checkout 3c14ad5d1a8a36f8d36604d1bbd91515fb4416fa
ln -s lib/torch_ngp dir_to/torch_ngp

4. Download LitePose checkpoints and puth them in lib/LitePose/ckpt

5. Download example dataset flame_steak_frms_time and put it in ./data

Usage

Train NeRF:

python jaws/run.py --config-name train_nerf data_dir=/path/to/dataset  xp_name=xp_name datamodule=jaws_dollyzoom.yaml

Launch JAWS

python jaws/run.py --config-name batch_jaws data_dir=path/to/data/dir/flame_steak_frms_time/ xp_name=xp_name jaws.target_dir=data/jaws_dolly_zoom_mask datamodule=jaws_dollyzoom.yaml

Render Images

python jaws/run.py --config-name render_jaws data_dir=path/to/data/dir/flame_steak_frms_time/ xp_name=xp_name jaws.target_dir=data/jaws_dolly_zoom_mask datamodule=jaws_dollyzoom.yaml render_target_dir=path/to/results/dir/final_res_n

Citation:

@InProceedings{Wang_2023_CVPR,
    author    = {Wang, Xi and Courant, Robin and Shi, Jinglei and Marchand, Eric and Christie, Marc},
    title     = {JAWS: Just a Wild Shot for Cinematic Transfer in Neural Radiance Fields},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2023},
}