What is it about?
This paper provides a comprehensive technical overview of how artificial intelligence is changing the way movie trailers are created. Historically, automatic trailer generation relied on simple, rigid engineering rules—such as tracking fast motion, detecting loud sounds, or evaluating where a viewer’s eyes naturally focus on a screen to chop out and string together interesting clips. We explore a massive industry shift toward true generative creativity. Today, modern AI systems use Large Language Models (LLMs) and advanced autoregressive Transformers to build trailers from the ground up. Instead of just cutting existing video footage chronologically, these newer tools can analyze entire films, write completely original voice-over scripts, match background music to emotional beats, and dynamically reorder scenes out of sequence to build narrative tension and excitement. Our paper breaks down these technical steps, introduces a new classification system for these AI tools, and looks closely at how technologies like OpenAI's Sora and Google's Veo impact content creators and media platforms.
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Why is it important?
Creating a movie trailer is incredibly different from just summarizing a video. A good summary tells you the plot to save you time, while a trailer is a persuasive art form meant to build intense curiosity and evoke specific emotions—often by intentionally hiding how a story ends. By detailing the shift from simple "clip selection" to "semantic synthesis," this research maps out the future of entertainment marketing. It highlights how automation will allow independent filmmakers and user-generated content (UGC) creators to instantly produce high-quality, studio-grade promotional videos. Additionally, it addresses critical ethical considerations and security challenges brought on by high-fidelity neural video synthesis, offering a balanced roadmap for developers, researchers, and filmmakers alike.
Perspectives
As researchers, we were fascinated by the unique tension between information coverage and emotional persuasion in filmmaking. Traditional video models are heavily penalized if they repeat footage or mix up the timeline. Yet, in cinema, repeating a visual motif (like a ticking clock) or jumping back and forth in time is exactly how you build suspense. Writing this paper allowed us to bridge the gap between hard data science and the subtle, creative nuances of cinematic storytelling, showing that AI is evolving from a simple utility tool into a collaborative creative partner.
Abhishek Dharmaratnakar
Read the Original
This page is a summary of: Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity, February 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3779211.3793162.
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