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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Google Colab notebook for running Nvidia flownet2-pytorch
Dockerized fast-artistic-videos
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.
Video Super Resolution with depth map and optical flow for unnatural object flow
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.
Improve performance of PWC-Net in foggy scenes
CrackPropNet
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks