Research on Multi Style Animation Character Generation Method Based on Deep Learning

Traditional animation character generation methods rely on manual drawing and style transfer algorithms, which have problems such as low efficiency, single style, and poor generalization ability. Therefore, this study proposes a multi style animation character generation framework based on deep learning, which combines Generative Adversarial Networks (GANs) and Style Adaptive Modules (SAMs) to achieve efficient and diverse animation character generation. The framework adopts a dual branch generator structure, which extracts character structure and style features separately, and generates the target style animation character through dynamic fusion strategy. The experiment shows that the proposed method has significantly better generation performance than traditional methods on various style datasets, with a 32% increase in style diversity score, a 5-fold increase in generation speed (single generation time shortened from 12 seconds to 2.4 seconds), and user satisfaction reaching 89.7%. This framework provides an efficient solution for intelligent design and multi style creation in the animation industry.