Whilst the final image quality might not be quite yet there, there is surely more to come from this extremely promising area of research in the future. We present an end-to-end learning approach for motion deblurring, which is based on conditional GAN and content loss. It improves the state-of-the art in terms of peak signal-to-noise…Read More
Month: December 2017
VFX Datasets
In almost all cases the success of supervised learning techniques is highly dependent on the quality and quantity of available training data. Perfect data is seldom available, and when it is there may not be sufficient diversity in it to train a hungry model. Cleaning noisy data can be labor-intesive if automated approaches are not…Read More
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept…Read More