🤖 AI Summary
Existing approaches to automatic video trailer generation predominantly rely on rule-based clip selection, which struggles to produce semantically coherent and emotionally compelling narratives. This work systematically reviews the technological evolution from heuristic selection to generative synthesis and introduces a novel taxonomy for AI-driven trailer generation tailored to the era of foundation models. We propose a unified framework that integrates autoregressive Transformers, multimodal large language models (MLLMs), text-to-video diffusion models (e.g., Sora, Veo), and graph convolutional networks (GCNs). This architecture enables controllable generation and semantic restructuring, substantially enhancing content creation efficiency on user-generated content (UGC) platforms, while also highlighting the ethical challenges posed by high-fidelity neural synthesis.
📝 Abstract
The domain of automatic video trailer generation is currently undergoing a profound paradigm shift, transitioning from heuristic-based extraction methods to deep generative synthesis. While early methodologies relied heavily on low-level feature engineering, visual saliency, and rule-based heuristics to select representative shots, recent advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), and diffusion-based video synthesis have enabled systems that not only identify key moments but also construct coherent, emotionally resonant narratives. This survey provides a comprehensive technical review of this evolution, with a specific focus on generative techniques including autoregressive Transformers, LLM-orchestrated pipelines, and text-to-video foundation models like OpenAI's Sora and Google's Veo. We analyze the architectural progression from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT), evaluate the economic implications of automated content velocity on User-Generated Content (UGC) platforms, and discuss the ethical challenges posed by high-fidelity neural synthesis. By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models, suggesting that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers.