# Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity

arXiv: https://arxiv.org/abs/2604.04953 · HTML: https://mlsanity.com/paper/2604.04953/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling](https://mlsanity.com/paper/2406.04321/) (2024)
- [A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming](https://mlsanity.com/paper/2404.16038/) (2024)
- [MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions](https://mlsanity.com/paper/2407.20962/) (2024)
- [PodReels: Human-AI Co-Creation of Video Podcast Teasers](https://mlsanity.com/paper/2311.05867/) (2023)
- [AI-Empowered Persuasive Video Generation: A Survey](https://mlsanity.com/paper/2112.09401/) (2021)
- [Towards Automated Movie Trailer Generation](https://mlsanity.com/paper/2404.03477/) (2024)
- [Sora OpenAI's Prelude: Social Media Perspectives on Sora OpenAI and the Future of AI Video Generation](https://mlsanity.com/paper/2403.14665/) (2024)
- [Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challenges](https://mlsanity.com/paper/2604.02211/) (2026)
- [TeaserGen: Generating Teasers for Long Documentaries](https://mlsanity.com/paper/2410.05586/) (2024)
- [A Comprehensive Survey on Generative AI for Video-to-Music Generation](https://mlsanity.com/paper/2502.12489/) (2025)
- [Vision-to-Music Generation: A Survey](https://mlsanity.com/paper/2503.21254/) (2025)
- [Taxonomy of the Retrieval System Framework: Pitfalls and Paradigms](https://mlsanity.com/paper/2601.20131/) (2026)
- [Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube Music](https://mlsanity.com/paper/2603.28994/) (2026)
- [AI based approach to Trailer Generation for Online Educational Courses](https://mlsanity.com/paper/2301.03957/) (2023)
- [An Automatic Deep Learning Approach for Trailer Generation through Large Language Models](https://mlsanity.com/paper/2601.23121/) (2026)
- [Trailer Reimagined: An Innovative, Llm-DRiven, Expressive Automated Movie Summary framework (TRAILDREAMS)](https://mlsanity.com/paper/2602.02630/) (2026)

## Built on by (most cited first, 2 of 2)

- [Retinal Cyst Detection from Optical Coherence Tomography Images](https://mlsanity.com/paper/2604.10843/) (2026)
- [Unified Agentic Video Editing Across Levels of Complexity and Creativity](https://mlsanity.com/paper/2609.12769/) (2026)

## Abstract (arXiv)

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.