Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Improve performance of PWC-Net in foggy scenes

Dockerized fast-artistic-videos

Video Super Resolution with depth map and optical flow for unnatural object flow

Consist of four different approaches for generating optical flow and can be demonstrated in Colab.

In this repository, we deal with the task of video frame interpolation with estimated optical flow. To estimate the optical flow we use pre-trained FlowNet2 deep learning model and experiment by fine-tuning it. We explore the interpolation performance on Spheres dataset and Corridor dataset.