FilMaster：融合電影製作原則與生成式AI，實現自動化影片生成
FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation
June 23, 2025
作者: Kaiyi Huang, Yukun Huang, Xintao Wang, Zinan Lin, Xuefei Ning, Pengfei Wan, Di Zhang, Yu Wang, Xihui Liu
cs.AI
摘要
AI驅動的內容創作在電影製作中展現了潛力。然而，現有的電影生成系統在實現電影原則方面存在困難，因此無法生成專業品質的影片，特別是在多樣化的鏡頭語言和電影節奏方面表現不足。這導致了模板化的視覺效果和缺乏吸引力的敘事。為了解決這一問題，我們引入了FilMaster，這是一個端到端的AI系統，它整合了現實世界的電影原則，用於生成專業級別的影片，並產出可編輯的、符合行業標準的輸出。FilMaster基於兩個關鍵原則：(1)從大量的現實世界電影數據中學習攝影技術，(2)模擬專業的、以觀眾為中心的後期製作工作流程。受這些原則的啟發，FilMaster包含了兩個階段：參考引導生成階段，將用戶輸入轉化為視頻片段；以及生成後期製作階段，通過協調視覺和聽覺元素來實現電影節奏，將原始素材轉化為視聽輸出。我們的生成階段突出了一個多鏡頭協同RAG鏡頭語言設計模塊，通過從440,000個電影片段的大型語料庫中檢索參考片段，來引導AI生成專業的鏡頭語言。我們的後期製作階段通過設計一個以觀眾為中心的電影節奏控制模塊，包括基於模擬觀眾反饋的粗剪和精剪過程，來模擬專業工作流程，以實現視聽元素的有效整合，從而創造出引人入勝的內容。該系統由生成式AI模型如(M)LLMs和視頻生成模型驅動。此外，我們引入了FilmEval，這是一個用於評估AI生成影片的綜合基準。大量實驗表明，FilMaster在鏡頭語言設計和電影節奏控制方面表現優異，推動了生成式AI在專業電影製作中的應用。
English
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.