Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity
New survey details how models like OpenAI's Sora are transforming automated trailer generation from simple editing to narrative creation.
A team of researchers has published a seminal survey paper, 'Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity,' that maps the radical transformation of automated trailer creation. The work details the industry's shift from legacy systems that relied on rule-based heuristics and low-level feature engineering to select clips, toward modern generative AI pipelines. These new systems, orchestrated by Large Language Models (LLMs) and powered by foundation models like OpenAI's Sora and Google's Veo, don't just find key moments—they synthesize coherent, emotionally resonant narratives from scratch.
The paper provides a comprehensive technical review, analyzing architectural progress from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT). It evaluates the economic impact of this 'automated content velocity' for User-Generated Content (UGC) platforms and discusses the significant ethical challenges posed by high-fidelity neural synthesis. By synthesizing recent literature, the authors establish a new taxonomy for the field, arguing that the future lies in moving beyond extractive selection to fully generative editing and semantic reconstruction of promotional video content.
- Details the shift from heuristic-based clip selection to generative AI synthesis using models like Sora and Veo.
- Establishes a new technical taxonomy and analyzes architectures from GCNs to Trailer Generation Transformers (TGT).
- Discusses economic impact on UGC platforms and ethical challenges of neural synthesis for promotional content.
Why It Matters
This research defines the future of automated video marketing, where AI doesn't just edit but creates compelling narratives from scratch.