Paper | Youtube

Official PyTorch implementation of Future Frame Synthesis for Fast Monte Carlo Rendering.

Future Frame Synthesis for Fast Monte Carlo Rendering

Zhan Li¹, Carl S Marshall², Deepak S Vembar², Feng Liu ¹,
¹Portland State University, ²Intel
Graphic Interface 2022

Set up environment with conda (ubuntu only)

conda create --name nfe python
conda activate nfe
pip install -r requirements.txt

checked with python 3.10.4 and conda 4.12.0

Download demo data folder with pretrained weight.

data folder place unzipped data folder under NFE, so it would be like

NFE
├── data
├── code

Run inference demo

export NFEDIR=$(pwd); # add NFEDIR 
cd code; # cd to code dir
python main_sq.py --eval  --write_images --mywrite 1 --val_n_batches -1 --resume $NFEDIR"/data/modelweight/br_0.3-cr_0.3-mlv_1-limgain_0-L_3-mask_F-phase_fused-pr_0.3-tr_0.5/best.pt.tar" --threshold 0.4 --br 0.3 --cr 0.3 --model FPCGE --phase fused --pr 0.3 --save $NFEDIR"/data/output" --tr 0.5 --train_file $NFEDIR"/data/trainingset" --val_file $NFEDIR"/data/input" --timing 1

Image results will be saved at data/output/FPCGE directory

Citation

If you find this repository useful for your research, please use the following.

@inproceedings{
li2022future,
title={Future Frame Synthesis for Fast Monte Carlo Rendering},
author={Zhan Li and Carl S Marshall and Deepak S Vembar and Feng Liu},
booktitle={Graphics Interface 2022},
year={2022},
url={https://openreview.net/forum?id=HI9zjeYVaG9}
}

Acknowledgments

This project is supported by a gift from Intel.

The codes are heavily based on SDC-Net: Video prediction using spatially-displaced convolution and Improving Semantic Segmentation via Video Propagation and Label Relaxation.

Our another interesting Fast Monte Carlo Rendering Paper.
Fast Monte Carlo Rendering via Multi-Resolution Sampling