Effect Enhancement of Generative Adversarial Networks in Movie Special Effects Generation

Although the existing special effects generation methods can generate relatively realistic special effects, there are still certain difficulties in detail control, resulting in special effects often not accurately reflecting the director's intention. To address this problem, this paper introduces generative adversarial networks (GANs) as a new film special effects generation technology to improve the ability to control the details of special effects images. GAN trains the generator and discriminator through adversarial training to achieve precise control of details such as texture, color changes, and dynamic effects during the special effects generation process. GAN optimizes the performance of details such as image superresolution, image restoration and enhancement, virtual character generation and synthesis, and dynamic special effects generation through unsupervised learning. Experimental results show that GAN can significantly improve the accuracy of detail recovery.