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FilmComposer: LLM-Driven Music Production for Silent Film Clips

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arxiv 2503.08147 v1 pith:MTXR3WWR submitted 2025-03-11 cs.CV cs.MMcs.SDeess.AS

classification cs.CVcs.MMcs.SDeess.AS
keywords musicfilmcomposerfilmproductionclipsprofessionalproposeactual
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In this work, we implement music production for silent film clips using LLM-driven method. Given the strong professional demands of film music production, we propose the FilmComposer, simulating the actual workflows of professional musicians. FilmComposer is the first to combine large generative models with a multi-agent approach, leveraging the advantages of both waveform music and symbolic music generation. Additionally, FilmComposer is the first to focus on the three core elements of music production for film-audio quality, musicality, and musical development-and introduces various controls, such as rhythm, semantics, and visuals, to enhance these key aspects. Specifically, FilmComposer consists of the visual processing module, rhythm-controllable MusicGen, and multi-agent assessment, arrangement and mix. In addition, our framework can seamlessly integrate into the actual music production pipeline and allows user intervention in every step, providing strong interactivity and a high degree of creative freedom. Furthermore, we propose MusicPro-7k which includes 7,418 film clips, music, description, rhythm spots and main melody, considering the lack of a professional and high-quality film music dataset. Finally, both the standard metrics and the new specialized metrics we propose demonstrate that the music generated by our model achieves state-of-the-art performance in terms of quality, consistency with video, diversity, musicality, and musical development. Project page: https://apple-jun.github.io/FilmComposer.github.io/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dialogue-Aware Video-to-Music Generation Using Public Domain Film Collections

    cs.SD 2026-08 conditional novelty 6.0 of 10

    A new public-domain film dataset and a frame-by-frame dialogue-conditioning module improve video-to-music generation on paired-fidelity metrics.

  2. AudioGenie: A Training-Free Multi-Agent Framework for Diverse Multimodality-to-Multiaudio Generation

    cs.SD 2025-05 conditional novelty 6.0 of 10

    A training-free multi-agent framework that decomposes multimodal inputs into audio events, selects specialized generators, and self-corrects outputs to produce multiple audio types.

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