A Study on the Relationship between Painter's Psychology and Anime Creation Style Based on a Deep Neural Network

Abstract

We conduct an in-depth study on the model construction of deep neural networks and design a model of painter's psychology and anime creation style to realize the study of the relationship between painter's psychology and anime creation style based on deep neural networks. This paper proposes an animation creation psychology classification algorithm that integrates human cognitive deep network structure optimization. The algorithm analyzes the connection between different convolutional layer features and animation characteristics through animation creation style CNN feature visualization. It interactively uses the knowledge of animation creation psychology expression techniques to optimize the network structure. This paper proposes a scene animation network based on spectral difference perception style. By analyzing the characteristics and differences in the spectrum between realistic and anime domain images, the generator is guided to learn the mapping relationships better to fit the style distribution of anime domain images. This paper uses a fully convolutional structure; the network is more lightweight and supports image inputs of arbitrary size, which can keep the semantic system of the background unchanged while highly deforming the five facial features, moving toward the goal of human-scene fusion for the animation task.