Research on the Optimization Model of Computer Deep Learning in the Production of Film and Television Special Effects

Application and optimization effect in film and television special effects production. This paper discusses how to use variational autoencoder (VAE), adversarial generation network (GAN), 3D model generation algorithm and random decision forest to innovate the film and television special effects production process. In this paper, we propose a VAE-based model for the generation of special effect elements, which realizes efficient compression and diversified special effect generation through latent spatial representation. Adversarial Generation Network (GAN) is used for style transfer, and adversarial training of generators and discriminators ensures the realism and stylistic consistency of special effect images. The 3D model generation algorithm combined with the Deep Convolutional Generative Adversarial Network (DCGAN) realizes the high-precision automatic modeling of 3D models. Stochastic decision forests are applied to animation sequence prediction, which improves the accuracy and coherence of prediction.