Research on Intelligent Poster Generation System Integrating Computer Artificial Intelligence Technology for Visual Communication Design

This paper aims to explore the application of computer artificial intelligence technology in the field of visual communication design, especially the research and development of intelligent poster generation system. We propose an innovative algorithm that integrates the benefits of variational autoencoders (VAE) and generative adversarial networks (Gans) to enable more efficient and flexible poster design generation. As a powerful generative model, variational autoencoders can generate new samples by learning potential representations of data. The generative adversarial network, with its excellent generative ability and adversarial training mechanism, has made remarkable achievements in the field of image generation. By integrating the two, we have built an intelligent system capable of capturing complex features of poster designs and producing diverse, high-quality posters. In the algorithm implementation, we use variational autoencoder to encode the poster design data and extract the potential feature representation. These features are then fed into a generator in the generative adversarial network to generate a new poster design through adversarial training. At the same time, we designed an efficient loss function to balance the diversity and authenticity of the generated posters. The experimental results show that the algorithm has excellent performance in poster design generation. It can not only generate creative and personalized posters, but also customize the design according to user needs. In addition, the algorithm also shows good generalization ability when dealing with different styles and themes of poster design. In the future, we will continue to optimize algorithms, explore more application scenarios, and promote the deep integration of computer artificial intelligence and visual communication design.