Image Generation of Animation Drawing Robot Based on Knowledge Distillation and Semantic Constraints

Abstract

With the development of robot technology, animation drawing robots have gradually appeared in the public eye. Animation drawing robots can generate many types of images, but there are also problems such as poor quality of generated images and long image drawing time. In order to improve the quality of images generated by animation drawing robots, an animation face line drawing generation algorithm based on knowledge distillation was designed to reduce computational complexity through knowledge distillation. To further raise the quality of images generated by robots, the research also designed an unsupervised facial caricature image generation algorithm based on semantic constraints, which uses facial semantic labels to constrain the facial structure of the generated images. The outcomes denote that the max values of the peak signal-to-noise ratio and feature similarity index measurements of the line drawing generation model are 39.45 and 0.7660 respectively, and the mini values are 37.51 and 0.7483 respectively. The average values of the gradient magnitude similarity bias and structural similarity of the loss function used in this model are 0.2041 and 0.8669 respectively. The max and mini values of Fréchet Inception Distance of the face caricature image generation model are 81.60 and 71.32 respectively, and the max and mini time-consuming values are 15.21s and 13.24s respectively. Both the line drawing generation model and the face caricature image generation model have good performance and can provide technical support for the image generation of animation drawing robots.