Gaussian Shadow Casting for Neural Characters

Paper

Gaussian Shadow Casting for Neural Characters
Luis Bolanos, Shih-Yang Su, and Helge Rhodin
CVPR 2024

Setup

conda create -n gsc python=3.9
conda activate gsc

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

pip install -r requirements.txt

Dataset

To showcase the learning of a neural avatar in intense outdoor illumination, while jointly learning the light source direction, we can train on the RANA dataset (subject 1). Config: configs/danbo_gsc_lo_rana.yaml

Download the RANA dataset from https://nvlabs.github.io/RANA/ and preprocess the data into our h5 format using the notebook rana_to_h5.ipynb. The notebook requires the SMPL_NEUTRAL.pkl file.

To showcase relighting on neural avatars learned in uniformly lit environments (ie. MonoPerfCap) please reach out to luisb[at]cs.ubc.ca or shihyang[at]cs.ubc.ca for the pretrained models as we are unable to share the pre-processed datasets due to licensing terms. Config for training while optimizing Gaussian lighting model: configs/npc_aniso_gaussians.yaml

Training

RANA dataset:

python train.py --config-name danbo_gsc_lo_rana expname=RANA_s1 dataset.subject=subject_01

MonoPerfCap dataset:

python train.py --config-name npc_aniso_gaussians expname=PerfCap_nadia dataset.subject=nadia

Relighting

RANA dataset:

python run_render.py --config-name rana model_config=logs/RANA_s1/config.yaml +ckpt_path=[PATH TO .th CHECKPOINT] output_path=[PATH TO OUTPUT FOLDER]

MonoPerfCap dataset:

python run_render.py --config-name perfcap_relight model_config=logs/PerfCap_nadia/config.yaml +ckpt_path=[PATH TO .th CHECKPOINT] output_path=[PATH TO OUTPUT FOLDER]

Results

Training Joint Optimization (Neural Field, Gaussians, Lighting)

Novel Pose Synthesis

Synthetic Sequence:

Real Sequence:

HDRi Relighting

HDRis from https://polyhaven.com/hdris

autumn_field_4k: veranda_4k: kiara_8_sunset_4k: tricolor_points (custom):

Citation

@inproceedings{bolanos2024gsc,
    title={Gaussian Shadow Casting for Neural Characters},
    author={Bolanos, Luis and Su, Shih-Yang and Rhodin, Helge},
    booktitle={The Conference on Computer Vision and Pattern Recognition},
    year={2024}
}

@inproceedings{su2023npc,
    title={NPC: Neural Point Characters from Video},
    author={Su, Shih-Yang and Bagautdinov, Timur and Rhodin, Helge},
    booktitle={International Conference on Computer Vision},
    year={2023}
}

@inproceedings{su2022danbo,
    title={DANBO: Disentangled Articulated Neural Body Representations via Graph Neural Networks},
    author={Su, Shih-Yang and Bagautdinov, Timur and Rhodin, Helge},
    booktitle={European Conference on Computer Vision},
    year={2022}
}

Acknowledgements

This work was supported in part by an NSERC Discovery Grant, an NSERC CGS-M Grant, and the computational resources and services provided by Advanced Research Computing at The University of British Columbia.