Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity
Summary: arXiv:2604.04953v1 Announce Type: cross
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.
Evolution of Video Trailer Generation
This survey provides a comprehensive technical review of the evolution in video trailer generation, focusing on generative techniques that have transformed the landscape:
- Autoregressive Transformers: These models have revolutionized the way narratives are constructed, enabling the generation of trailers that resonate with audiences on emotional levels.
- LLM-Orchestrated Pipelines: By integrating various models, these pipelines streamline the trailer creation process, allowing for seamless transitions between video content and narrative elements.
- Text-to-Video Foundation Models: Notable examples include OpenAI’s Sora and Google’s Veo, which utilize advanced algorithms to convert textual descriptions into compelling video trailers.
Architectural Progression
We analyze the architectural progression from traditional methods to cutting-edge technologies:
- Graph Convolutional Networks (GCNs): Initially used for analyzing relationships in video content, GCNs provided a framework for understanding shot selection.
- Trailer Generation Transformers (TGT): This new architecture facilitates the generation of coherent trailers through a deep understanding of both visual and textual data.
Economic Implications and User-Generated Content
As automation in content creation accelerates, the economic implications on User-Generated Content (UGC) platforms are substantial:
- Increased content velocity may enhance user engagement, leading to a more dynamic platform environment.
- However, it raises questions about content authenticity and creator recognition, as AI-generated content may dominate the space.
Ethical Challenges
The rise of high-fidelity neural synthesis presents several ethical challenges:
- Content Ownership: As AI systems generate trailers, the question of intellectual property rights becomes increasingly complex.
- Misinformation Risks: The potential for creating misleading or manipulated content poses risks to audiences and creators alike.
Conclusion
By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models. It suggests that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers. As the technology continues to evolve, stakeholders must navigate the balance between innovation and ethical considerations in the realm of automated content creation.