Cross-view image translation is challenging because it involves images with drastically
different views and severe deformation. In this paper, we propose a novel approach named …

The problem of estimating scene-depth from a single image has seen great progress lately.
Recent unsupervised methods are based on view-synthesis and learn depth by minimizing …

With the popular application of convolutional neural networks in computational intelligence,
research on deep learning-based view synthesis has been a hot topic. Although promising …

It is hard to generate an image at target view well for previous cross-view image translation
methods that directly adopt a simple encoder-decoder or U-Net structure, especially for …

In this paper we address the benefit of adding adversarial training to the task of monocular
depth estimation. A model can be trained in a self-supervised setting on stereo pairs of …

Novel view synthesis is an important problem in computer vision and graphics. Over the
years a large number of solutions have been put forward to solve the problem. However, the …

We present a novel view synthesis method based upon latent voxel embeddings of an
object, which encode both shape and appearance information and are learned without …

We address the problem of view synthesis in complex outdoor scenes. We propose a novel
convolutional neural network architecture that includes flow-based and direct synthesis sub …

Abstract Systems, methods, and non-transitory computer-readable media are disclosed for
utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an …

Abstract Systems, methods, and non-transitory computer-readable media are disclosed for
utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an …