An Automatic Deep Learning Approach for Trailer Generation through Large Language Models

Trailers are short promotional videos designed to provide audiences with a glimpse of a movie. The process of creating a trailer typically involves selecting key scenes, dialogues and action sequences from the main content and editing them together in a way that effectively conveys the tone, theme and overall appeal of the movie. This often includes adding music, sound effects, visual effects and text overlays to enhance the impact of the trailer. In this paper, we present a framework exploiting a comprehensive multimodal strategy for automated trailer production. Also, a Large Language Model (LLM) is adopted across various stages of the trailer creation. First, it selects main key visual sequences that are relevant to the movie’s core narrative. Then, it extracts the most appealing quotes from the movie, aligning them with the trailer’s narrative. Additionally, the LLM assists in creating music backgrounds and voiceovers to enrich the audience’s engagement, thus contributing to make a trailer not just a summary of the movie’s content but a narrative experience in itself. Results show that our framework generates trailers that are more visually appealing to viewers compared to those produced by previous state-of-the-art competitors.

💡 Research Summary

The paper presents a comprehensive multimodal framework that automates the creation of movie trailers by leveraging a large language model (LLM), specifically OpenAI’s GPT‑4, together with a suite of open‑source tools for video, audio, and text processing. The authors argue that traditional trailer production is labor‑intensive, requiring coordinated effort across editing, sound design, and narrative crafting, and that existing automated approaches focus mainly on visual summarization or simple shot selection without integrating narrative coherence, dialogue, or music.
The proposed pipeline is divided into four stages: Preparation, Visual, Voice‑Over, and Soundtrack. In the Preparation stage, movie metadata (synopsis, quotes, director, release date) are scraped from IMDB using the Cinemagoer library, and frames are extracted from the video at a fixed interval (every nine seconds) using FFmpeg. The LLM then refines the synopsis into a series of “visual sub‑plots,” each described in simple, concrete language to facilitate later visual matching.
The Visual stage creates two types of clips. “Quote Clips” are built from the most impactful movie lines. After cleaning and filtering the scraped quotes, the LLM evaluates them for thematic relevance and emotional intensity (using TextBlob sentiment scores). StableWhisper is used to locate the corresponding audio segments, and Pyannote’s voice‑activity detection refines the boundaries. Shot‑boundary detection (SBD) is applied to ensure that each Quote Clip contains a coherent visual segment, and orphan shots are padded with black frames to preserve continuity. “Standard Clips” are generated by extracting keywords from each sub‑plot, embedding both the keywords and all video frames with the CLIP‑ViT‑L‑14 model, and selecting frames with high cosine similarity. A minimum temporal distance (1.5 % of total movie length) is enforced to guarantee coverage across the film. EasyOCR and CRNN verify that selected frames are free of overlaid text. A buffered zone around each selected frame is then expanded using SBD to capture the full context, producing visually smooth Standard Clips.
In the Visual Trailer Assembly step, Standard and Quote Clips are interleaved (e.g., SC‑QC‑SC‑QC‑SC) and audio transitions are smoothed with fade‑in/out effects. A timestamp log records the exact placement of each Quote Clip for later audio editing.
The Voice‑Over stage uses the LLM to generate a narration script that references the plot, director, and release month while avoiding spoilers. The script length matches the trailer duration, and the generated text is fed to a text‑to‑speech engine to produce a synchronized voice‑over track.
The Soundtrack stage asks the LLM to specify the mood, tempo, and length of background music. These specifications are passed to an existing music generation model, completing the auditory layer of the trailer.
The final output is a single video file that combines Standard Clips, Quote Clips, voice‑over narration, and background music. The authors evaluate their system against two state‑of‑the‑art automatic trailer generators, Movie2trailer and PPBV‑AM, using both subjective user surveys (visual appeal, narrative coherence, emotional impact) and objective metrics (clip diversity, transition smoothness). Across multiple genres, the proposed framework consistently outperforms the baselines, especially in perceived narrative depth and emotional resonance.
Limitations acknowledged include the inability to publicly share most generated trailers due to copyright, a relatively small test set, reliance on the commercial GPT‑4 API (introducing cost and latency concerns), and potential cultural or linguistic biases inherent in the LLM. Future work is suggested to scale the evaluation, explore open‑source LLM alternatives for cost reduction, and personalize trailer generation for specific audience segments.
In summary, the paper makes a notable contribution by integrating a powerful LLM throughout the entire trailer creation pipeline, achieving a level of narrative and emotional sophistication that surpasses prior automated methods while remaining implementable with publicly available tools.