Generator, generator training method, and method for avoiding image coordinate adhesion

Abstract

Disclosed are a gated network-based generator, a generator training method, and a method for avoiding image coordinate adhesion. The generator processes, by using an image input layer, a to-be-processed image as an image sequence and inputs it to a feature encoding layer. Multiple feature encoding layers encode the image sequence by using a gated convolutional network, to obtain an image code. Moreover, multiple image decoding layers decode the image code by using an inverse gated convolution unit, to obtain a target image sequence. Finally, an image output layer splices the target image sequence to obtain a target image. Therefore, a character feature in the obtained target image is more obvious, making details of a facial image of generated digital human more vivid, whereby solving a problem of image coordinate adhesion in a digital human image generated by an existing generator using a generative adversarial network, and improving user experience.