Generative AI in Cinematic Production: Practical Applications, Technical Bottlenecks and Industry Evolution Paths
DOI:
https://doi.org/10.54097/tntjas45Keywords:
Generative Artificial Intelligence, Diffusion Models, Film Production Pipeline, Video Synthesis, Neural Rendering, Virtual ProductionAbstract
The global film industry is experiencing a fundamental transformative revolution driven by artificial intelligence technologies. Although image generation rooted in deep learning first emerged with Generative Adversarial Networks (GANs) in 2014, it was the large-scale application of diffusion models around 2020 that triggered a qualitative breakthrough in photorealistic visual content creation. From 2022 to 2025, commercial platforms and open-source weight frameworks represented by Midjourney, DALL-E 2, Stable Diffusion, Sora and Runway Gen-3 have continuously verified that algorithm-generated visuals can achieve parity with traditional visual effects in terms of fidelity, and even outperform manual production in certain standardized workflows. For film practitioners, this rapid technological iteration has reshaped the entire production chain. Traditional filmmaking depends on highly fragmented and labor-intensive processes, covering storyboard creation and location reconnaissance in pre-production, lighting design and camera operation in principal photography, as well as fine-grained color grading and VFX compositing in post-production. As generative AI penetrates each independent link, it brings significant advantages in shortening production cycles and cutting costs. Meanwhile, it has also sparked fierce industry discussions on artistic authorship, skilled labor substitution and the essential nature of visual storytelling. At present, a large number of computer science studies focus on the technical breakthroughs of single video generation algorithms, but there is an obvious research gap in the practical evaluation of these tools from the perspective of professional film production. Existing review articles mostly analyze model architectures in isolation or discuss multimedia applications in a broad sense, ignoring the unique workflow requirements of cinematography. To fill this gap, this paper constructs a functional classification system of generative AI based on the full life cycle of film production, prioritizing application scenarios over algorithmic structures. By evaluating mainstream platforms against professional standards such as output controllability and hardware compatibility, this paper identifies the core technical obstacles in current applications and proposes a practical human-AI collaborative creation framework that preserves directorial creative intention.
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