Abstract: Systems, apparatuses and methods may provide for technology that estimates poses of a plurality of input images, reconstructs a proxy three-dimensional (3D) geometry based on the estimated poses and the plurality of input images, detects a user selection of a virtual viewpoint, encodes, via a first neural network, the plurality of input images with feature maps, warps the feature maps of the encoded plurality of input images based on the virtual viewpoint and the proxy 3D geometry, and blends, via a second neural network, the warped feature maps into a single image, wherein the first neural network is deep convolutional network and the second neural network is a recurrent convolutional network.

Abstract: Systems, apparatuses and methods may provide for technology that estimates poses of a plurality of input images, reconstructs a proxy three-dimensional (3D) geometry based on the estimated poses and the plurality of input images, detects a user selection of a virtual viewpoint, encodes, via a first neural network, the plurality of input images with feature maps, warps the feature maps of the encoded plurality of input images based on the virtual viewpoint and the proxy 3D geometry, and blends, via a second neural network, the warped feature maps into a single image, wherein the first neural network is deep convolutional network and the second neural network is a recurrent convolutional network.