A Context-Aware Image Generation Method for Assisted Design of Movie Posters Using Generative Adversarial Network

Considering the continuous development of the film industry and the improvement of the living standard among people, movies have gradually come to the civilians. A good movie poster can effectively reflect the content of the movie, attract the audience, stimulate the demand and achieve a good publicity effect. The current movie poster design work is mainly carried out by professional designers, which requires a lot of time and labor cost. In this paper, we propose a context-aware image generation method for assisted design of movie posters using generative adversarial network (named as MPAD-CIP for short). First, the basic information and visual contents of the movie are perceived, and the representative images are extracted, with the use of convolution operations. Then, a backbone network of deep convolutional generative neural network is formulated to generate images for summary of movies. The backbone network is composed of two components: a generator and a discriminator. Their combination realizes the computer-assisted movie poster design by sensing visual context. In the experimental part, the proposed MPAD-CIP method is compared with several benchmark models to demonstrate that the posters generated by this paper are more realistic and versatile, and some of the generated posters are exhibited.