基于SpeConv深度学习的视频补帧 插帧.zip

# Introduction This project is updated from [dagf2101/pytorch-sepconv](https://github.com/dagf2101/pytorch-sepconv) updates: 1. some code robustness 2. now the output video has audio 3. increase a progress bar when processing **Chinese introduction and practice lesson video:** [【中文】 基于SepConv深度学习的视频补帧 插帧 论文+代码+实战教程](https://www.bilibili.com/video/av29090385/) ## Warning!!! The code is old and has bugs,that it needs a high calculation capability GPU（`K80` is not enough）to run successfully. Also it is not friendly to Windows users. So u should refer to the origin author [sniklaus/pytorch-sepconv](https://github.com/sniklaus/pytorch-sepconv) to get newest code which used cupy to compile automatically. But it can only handle photos. > Below is the origin 'readme' ------------------------------ # pytorch-sepconv This is a reference implementation of Video Frame Interpolation via Adaptive Separable Convolution [1] using PyTorch. Given two frames, it will make use of <a href="http://graphics.cs.pdx.edu/project/adaconv">adaptive convolution</a> [2] in a separable manner to interpolate the intermediate frame. Should you be making use of our work, please cite our paper [1]. <a href="https://arxiv.org/abs/1708.01692" rel="Paper"><img src="http://content.sniklaus.com/SepConv/Paper.jpg" alt="Paper" width="100%"></a> For the Torch version of this work, please see: https://github.com/sniklaus/torch-sepconv ## setup To build the implementation and download the pre-trained networks, run `bash install.bash` and make sure that you configured the `CUDA_HOME` environment variable. After successfully completing this step, run `python run.py` to test it. Should you receive an error message regarding an invalid device function during execution, configure the utilized CUDA architecture within `install.bash` to something your graphics card supports. ## usage To run it on your own pair of frames, use the following command. You can either select the `l1` or the `lf` model, please see our paper for more details. Image: ``` python run.py --model lf --first ./images/first.png --second ./images/second.png --out ./result.png ``` Video: ``` python run.py --model lf --video ./video.mp4 --video-out ./result.mp4 ``` ## video <a href="http://web.cecs.pdx.edu/~fliu/project/sepconv/demo.mp4" rel="Video"><img src="http://web.cecs.pdx.edu/~fliu/project/sepconv/screen.jpg" alt="Video" width="100%"></a> ## license The provided implementation is strictly for academic purposes only. Should you be interested in using our technology for any commercial use, please feel free to contact us. ## references ``` [1] @inproceedings{Niklaus_ICCV_2017, author = {Simon Niklaus and Long Mai and Feng Liu}, title = {Video Frame Interpolation via Adaptive Separable Convolution}, booktitle = {IEEE International Conference on Computer Vision}, year = {2017} } ``` ``` [2] @inproceedings{Niklaus_CVPR_2017, author = {Simon Niklaus and Long Mai and Feng Liu}, title = {Video Frame Interpolation via Adaptive Convolution}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2017} } ``` ## acknowledgment This work was supported by NSF IIS-1321119. The video above uses materials under a Creative Common license or with the owner's permission, as detailed at the end.