A Neural Network System for Optimizing Film Special Effects

This paper explores a neural network system designed to optimize film visual effects in mid-sized studios. Using a deep learning approach with convolutional neural networks and attention mechanisms, the system addresses inefficiencies in manual workflows, such as inconsistent actor–computer-generated image interactions and excessive lighting rework. The case study focuses on the 2024 fantasy short Mountain of Mist, produced by Nanjing's Cloud Frame VFX Studio, with a ¥500,000 budget. The system utilizes cloud deployment on Amazon Web Services p3.8xlarge instances and human–artificial intelligence collaboration in which artificial intelligence generates base frames and artists add creative refinements. For key visual effects elements like dynamic “ghost mist” and bioluminescent undergrowth, the system reduced production time by 85% and lighting rework by 83.3%, while achieving theatrical-grade quality (peak signal-to-noise ratio of 34.5 db; structural similarity index of 0.94). The findings demonstrate the system's potential to enhance precision, reduce costs, and improve scalability for mid-sized studios.