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

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arxiv 2506.18899 v1 pith:6ZKXBBY7 submitted 2025-06-23 cs.CV

classification cs.CV
keywords cinematicfilmgenerationfilmasterprinciplescameragenerativelanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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

  1. FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling

    cs.CV 2026-07 conditional novelty 7.0 of 10

    FilmWorld generates multi-scene films from novels by materializing an explicit evolving world-state trajectory and rendering shots in parallel, beating five agents on its own FilmEval benchmark.

  2. The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation

    cs.CV 2026-01 reject novelty 5.0 of 10

    An agentic dialogue-to-video pipeline (ScripterAgent, DirectorAgent, CriticAgent) claims to improve long-horizon cinematic coherence, but its supporting evaluation is partly self-referential.

  3. PersonaVlog: Personalized Multimodal Vlog Generation with Multi-Agent Collaboration and Iterative Self-Correction

    cs.CV 2025-08 conditional novelty 5.0 of 10

    PersonaVlog auto-generates personalized vlogs from a theme and reference image using multimodal agents with a feedback-rollback loop, and introduces the ThemeVlogEval benchmark.

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