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Bridging Cinematic Principles and Generative AI for Automated Film Generation (arxiv.org)
34 points by jag729 on Jun 24, 2025 | hide | past | pdf | 7 comments on HN

In plain words: FilMaster turns a user's idea into a finished film by copying real camera moves from 440,000 clips, then editing the footage like a pro, using simulated audience reactions to set the pacing. It beat other AI film systems on camera work and rhythm.

Abstract · FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation

AI-driven content creation has shown potential in film production. However, existing film generation systems struggle to implement cinematic principles and thus fail to generate professional-quality films, particularly lacking diverse camera language and cinematic rhythm. This results in templated visuals and unengaging narratives. To address this, we introduce FilMaster, an end-to-end AI system that integrates real-world cinematic principles for professional-grade film generation, yielding editable, industry-standard outputs. FilMaster is built on two key principles: (1) learning cinematography from extensive real-world film data and (2) emulating professional, audience-centric post-production workflows. Inspired by these principles, FilMaster incorporates two stages: a Reference-Guided Generation Stage which transforms user input to video clips, and a Generative Post-Production Stage which transforms raw footage into audiovisual outputs by orchestrating visual and auditory elements for cinematic rhythm. Our generation stage highlights a Multi-shot Synergized RAG Camera Language Design module to guide the AI in generating professional camera language by retrieving reference clips from a vast corpus of 440,000 film clips. Our post-production stage emulates professional workflows by designing an Audience-Centric Cinematic Rhythm Control module, including Rough Cut and Fine Cut processes informed by simulated audience feedback, for effective integration of audiovisual elements to achieve engaging content. The system is empowered by generative AI models like (M)LLMs and video generation models. Furthermore, we introduce FilmEval, a comprehensive benchmark for evaluating AI-generated films. Extensive experiments show FilMaster's superior performance in camera language design and cinematic rhythm control, advancing generative AI in professional filmmaking.

Kaiyi Huang, Yukun Huang, Xintao Wang, Zinan Lin, Xuefei Ning, Pengfei Wan, Di Zhang, Yu Wang, Xihui Liu
arXiv:2506.18899 · cs.CV · submitted Jun 23, 2025
abstract · pdf · html · Project Page: https://filmaster-ai.github.io/

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After clicking the button "View full video" I got:

"Could not play video. Please ensure you have interacted with the site first and try again."

PS: the cybertruck in the video looks a lot better than a real one

Really interesting, I know nothing about cinematography and don't really have the language to articulate what it is about their clips that are superior to other AI visuals, but it's definitely noticable, even if the overall quality of the generation isn't as high as Veo 3.
So if you click on the principle author's arXiv submission history, they appear to have dozens, perhaps hundreds, of announced and submitted papers in just the last two months. Is that normal?
Huang, K is a common name.

You need to search for the author's full name

https://arxiv.org/search/cs?query=Huang%2C+Kaiyi&searchtype=...

interesting -- I found it through recent submissions, so I'd be leery
actually I think this is a limitation of ArXiv -- clicking on the principal author links to a search for "K Huang," (which is a super common name). Limiting to the author's actual name only produces 6 results.