Motion prediction using one or more neural networks

Abstract

Animation can be generated with a high perceptive quality by utilizing a trained neural network that takes as input a current state of a virtual character to be animated and predict how this character would appear in one or more subsequent frames. Such a process can be performed recursively to generate the data for these frames. During training, each frame of a generated sequence can be predicted from a result for a previous frame, and this generated sequence can be compared with a ground truth sequence using a generative network. Differences between the ground truth and generated animation sequences can be minimized, whereby a specific objective function does not need to be manually defined. Minimizing differences between the generated animation sequences and ground truth sequences during training improves the quality of network predictions for single frames at inference time.