Junheum Park, Keunsoo Ko, Chul Lee, and Chang-Su Kim
Official PyTorch Code for "BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation" [paper]
- PyTorch 1.3.1 (Other versions can cause different results)
- CUDA 10.0
- CuDNN 7.6.5
- python 3.6
Create conda environment:
$ conda create -n BMBC python=3.6 anaconda
$ conda activate BMBC
$ pip install opencv-python
$ conda install pytorch==1.3.1 torchvision cudatoolkit=10.0 -c pytorch
Download repository:
$ git clone https://github.com/JunHeum/BMBC.git
Download pre-trained model parameters:
$ unzip BMBC_Weights.zip
Generate an intermediate frame at t=0.5 on your pair of frames:
$ python run.py --first images/im1.png --second images/im3.png --output images/im2.png
Generate an intermediate frame at arbitrary time t:
$ python run.py --first images/im1.png --second images/im3.png --output images/im2_025.png --time_step 0.25
Please cite the following paper if you feel this repository useful.
@inproceedings{BMBC,
author = {Park, Junheum and Ko, Keunsoo and Lee, Chul and Kim, Chang-Su},
title = {BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation},
booktitle = {European Conference on Computer Vision},
year = {2020}
}
See MIT License