DeepStereo: Learning to Predict New Views from the World’s Imagery

Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision, but their use in graphics problems has been limited. In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to…Read More

Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation

Although this technique to estimate camera motion and decompose the scene into rigid/dynamic motion (a potential aid to segmentation) relies on a depth channel in the input images, it may not be long until new approaches are developed which can operate on RGB only. Estimation of 3D motion in a dynamic scene from a temporal…Read More

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains…Read More

Learning to Segment Every Thing

Producing accurate pixel-level masks around specific objects within images is of course a common task in VFX. Current solutions can be labor-intensive, and the results from one task cannot be used directly to improve the quality of future work. Existing tools generally do not know the semantic context of the object whose mask is being…Read More

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

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

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