MoNeRF: Fast Non-Rigid Radiance Fields from Monocularized Data

| Project Page | Paper | Video | Dataset |

Official PyTorch Implementation of 'Fast Non-Rigid Radiance Fields from Monocularized Data'.

Changelog

• [28.02.25] Updated the code to be compatible with the official NeRFICG project release!

Getting Started

This project is built on the NeRFICG framework. Before cloning this repository, ensure the framework is set up:

• Follow the instructions in the Getting Started section of the main nerficg repository (tested with commit c8e258b, PyTorch 2.5).
• After setting up the framework, navigate to the top level directory:

   cd <Path/to/framework/>nerficg

• also make sure to activate the correct conda environment

   conda activate nerficg

Now, you can directly add this project as an additional method:

• clone this repository to the src/Methods/ directory:

   git clone git@github.com:MoritzKappel/MoNeRF.git src/Methods/MoNeRF

• install all dependencies and CUDA extensions for the new method using:

   ./scripts/install.py -m MoNeRF

Training and Inference

After setup, the MoNeRF method is fully compatible with all NeRFICG framework scripts in the scripts/ directory. This includes config file generation (defaultConfig.py), training (train.py), inference and performance benchmarking (inference.py), metric calculation (generateTables.py), and live rendering via the GUI (gui.py).

For guidance and detailed instruction, please refer to the main nerficg repository.

Dataset

To use our MMVA dataset, first download the zipped dataset and unpack it to the nerficg/dataset directory. Then copy the MMVA.py dataloader file to the nerficg/src/Datasets directory to make it available to the training and inference scripts.

License and Citation

This project is licensed under the MIT license (see LICENSE).

If you use this code for your research projects, please consider a citation:

@article{kappel2024fast,
  title = {Fast Non-Rigid Radiance Fields from Monocularized Data},
  author = {Kappel, Moritz and Golyanik, Vladislav and Castillo, Susana  and Theobalt, Christian and Magnor, Marcus},
  journal = {{IEEE} Transactions on Visualization and Computer Graphics ({TVCG})},
  doi = {10.1109/{TVCG}.2024.3367431},
  pages = {1--12},
  month = {Feb},
  year = {2024}
}