Abstract: Provided is a computer system that includes one or more processors and one or more non-transitory computer-readable media that collectively store a machine-learned image interpolation model. The machine-learned image interpolation model is configured to: extract, for each of multiple different scales, a respective set of feature values from each of a pair of input images; generate, for each of the multiple different scales, a respective flow estimate for each of the pair of input images that indicates a respective flow from the interpolation time to the respective capture time; warp, for each of the multiple different scales, the respective set of feature values for each of the pair of input images according to the respective flow estimate to generate respective warped sets of features; and generate a interpolated image based on the respective warped sets of features for the pair of input images and the multiple different scales.

Abstract: Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.

Abstract: Apparatuses, systems, and techniques to generate interpolated video frames. In at least one embodiment, an interpolated video frame is generated based, at least in part, on a first set of pixel data sampled from a first video frame, and a second set of pixel data sampled from a second video frame based, at least in part, on a set of forward pointing motion vectors from the first video frame to the second video frame.

Abstract: Apparatuses, systems, and techniques to generate interpolated video frames. In at least one embodiment, an interpolated video frame is generated based, at least in part, on one of a plurality of possible motions of one or more objects from a first video frame to a second video frame.

Abstract: Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.

Abstract: Apparatuses, systems, and techniques to enhance video. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having a higher frame rate, higher resolution, or reduced number of missing or corrupt video frames.