FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation
Kaiyi Huang, Yukun Huang, Xintao Wang, Zinan Lin, Xuefei Ning, Pengfei Wan, Di ZHANG, Yu Wang, Xihui Liu
摘要
Existing AI-based film generation systems can generate high-quality videos, but struggle to design expressive camera language and establish cinematic rhythm. This deficiency leads to templated visuals and unengaging narratives. To address these limitations, we introduce FilMaster, an end-to-end automated film generation system that integrates real-world cinematic principles to generate professional-grade, editable films. Inspired by professional filmmaking, FilMaster is built on two key cinematic principles: (1) camera language design by learning cinematography from extensive real-world film references, and (2) cinematic rhythm by emulating professional post-production workflows. For camera language, our Multi-shot Synergized Camera Language Design module introduces a novel scene-level Retrieval-Augmented Generation (RAG) framework. Unlike shot-level RAG which retrieves references independently and often leads to visual incoherence, our approach treats an entire scene, comprising multiple shots with a shared spatio-temporal context and narrative objective, as a single, unified query. This holistic query retrieves a consistent set of semantically similar shots with cinematic techniques from a large corpus of 440,000 real film clips. These references then guide an LLM to synergistically plan coherent and expressive camera language for all shots within that scene. To achieve cinematic rhythm, our Audience-Aware Cinematic Rhythm Control module emulates professional post-production, featuring a Rough Cut assembly followed by a Fine Cut process that uses simulated audience feedback to optimize the integration of video and sound for cinematic rhythm. Extensive experiments show superior performance in camera language and cinematic rhythm, paving the way for generative AI in professional filmmaking.
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引用它的顶会 Paper6
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- ViStoryBench: Comprehensive Benchmark Suite for Story VisualizationCailin Zhuang, Ailin Huang, Hu Yaoqi, Jingwei Wu 等CVPR 2026 · 被引用 37 次
- MoGA: Mixture-of-Groups Attention for End-to-End Long Video GenerationWeinan Jia, Yuning Lu, Mengqi Huang, Hualiang Wang 等ICLR 2026 · 被引用 14 次
- CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video GenerationKaiyi Huang, Yukun Huang, Yu Li, Jianhong Bai 等CVPR 2026 · 被引用 7 次
- LoCoT2V-Bench: Benchmarking Long-Form and Complex Text-to-Video GenerationXiangqing Zheng, CHENGYUE WU, Kehai Chen, Min zhangICML 2026 · 被引用 3 次
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