Abstract: Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to indicate frame size information using one or more neural networks.

Abstract: Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to enable frame interpolation to use one or more neural networks.

Abstract: Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to indicate support to use one or more neural networks to perform frame interpolation.

Abstract: Apparatuses, systems, and techniques to process image frames. In at least one embodiment, one or more neural networks are used to blend two or more video frames between a first video frame and a second video frame. In at least one embodiment, a blended video frame is used to generate an intermediate video frame between the first video frame and the second video frame.

Abstract: Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to cause frame interpolation to be performed using one or more neural networks.

Abstract: Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to disable frame interpolation to use one or more neural networks.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques to perform unsupervised keypoint or landmark learning using one or more neural networks. In at least one embodiment, one or more neural networks use pose and appearance information to construct a foreground and a background, which are then used to reconstruct an input image and determine loss values to train the one or more neural networks.

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.