INTEGRATING NEURAL NETWORKS INTO POST-PRODUCTION

Abstract

The article explores the integration of neural networks into the post-production process as a transformative response to digitalization, the rapid advancement of artificial intelligence, and the increasing demand for high-quality audiovisual content. Post-production, encompassing tasks such as editing, color correction, sound synthesis, and visual effects generation, is among the most resource-intensive stages of media production. The application of neural networks significantly optimizes technical operations while reshaping the creative process itself, enhancing efficiency, precision, and artistic quality.

The study highlights the capabilities of deep learning-based neural networks to perform complex post-production tasks traditionally requiring highly qualified specialists. Examples include automated color grading, visual artifact removal, background blurring, depth of field simulation, and style transfer. Generative models such as GANs are used to produce photorealistic animations, reconstruct damaged footage, and synthesize digital characters that mimic human emotion and movement. These technologies not only accelerate workflows but also open new horizons for creativity.

The global adoption of AI in post-production by major studios such as Netflix, Disney, and Warner Bros. has led to significant cost and time savings. AI tools automate routine tasks, support localization, improve dubbing quality, and assist in aesthetic decision-making. However, in Ukraine, AI integration remains fragmented and constrained by limited access to technology, high software costs, low awareness among professionals, insufficient institutional support, and underdeveloped legal frameworks for AI-generated content.

Despite these challenges, Ukrainian post-production companies are increasingly experimenting with AI tools, viewing them as strategic assets for global competitiveness. Access to open-source and freemium models has enabled even small studios to adopt AI at various stages, particularly for visual ideation, auto-cropping, and scene harmonization. The article underscores the need for systemic support through technological infrastructure, interdisciplinary education, and adaptive legal policies to ensure responsible and effective AI integration in Ukraine’s creative industries.