Juewen Peng1, Jianming Zhang2 Xianrui Luo1, Hao Lu1, Ke Xian1*, Zhiguo Cao1,
1Huazhong University of Science and Technology, 2Adobe Research
This repository is the official PyTorch implementation of the ECCV 2022 paper "MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects".
git clone https://github.com/JuewenPeng/MPIB.git
cd MPIB
pip install -r requirements.txt
Download pretrained models from Google Drive or Baidu Netdisk, and put them in the directory checkpoints
. Note that we use LaMa as our inpainting model, and inpnet.pth
orignates from https://github.com/saic-mdal/lama.
Run the following inference code.
python demo.py --image_path 'inputs/new_11_all-in-focus.jpg' --disp_path 'inputs/new_11_disparity.jpg' --save_dir 'outputs' --K 60 --disp_focus 207/255 --gamma 2.2
image_path
: path of the input all-in-focus imagedisp_path
: path of the input disparity map (predicted by DPT in this example)save_dir
: directory to save the resultsK
: blur parameterdisp_focus
: refocused disparity (range from 0 to 1)gamma
: gamma value
See demo.py
for more details.
If you find our work useful in your research, please cite our paper.
@inproceedings{Peng2022MPIB,
title = {MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects},
author = {Peng, Juewen and Zhang, Jianming and Luo, Xianrui and Lu, Hao and Xian, Ke and Cao, Zhiguo},
booktitle = {The Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022}
}