A systematic review for 2019\u20132025 on deep learning models in the film production industry<\/p>\n\n\n\n
Behnam\u00a0Yousefimehr,\u00a0Mehdi\u00a0Ghatee,\u00a0Shervin\u00a0Ghaffari,\u00a0Arash\u00a0Arasteh,\u00a0Parsa\u00a0Ahmadi,\u00a0Amirhossein\u00a0Ghane,\u00a0Sina\u00a0Esnaasharieh<\/p>\n\n\n\n
Entertainment Computing<\/a><\/p>\n\n\n\n Volume 56<\/a>, January 2026, 101076\u00a0<\/p>\n\n\n\n Highlights:<\/p>\n\n\n\n Abstract:<\/p>\n\n\n\n The integration of artificial intelligence (AI) into film production is fundamentally reshaping cinematic creation. This survey presents a systematic review of deep learning applications in the film industry from 2019 to 2025, analyzing their impact across four core pillars: Scriptwriting, Video Generation, Video Editing, and Music Generation. To overcome the limitations of subjective qualitative reviews, we introduce the\u00a0Intelligent Multi-Dimensional Paper Scoring Framework (IMPSF)<\/em>, a novel methodology for the quantitative and reproducible evaluation of scholarly literature. The IMPSF employs a multi-agent system to assess papers across four key dimensions: Technical Innovation, Empirical Rigor, Cinematic Applicability, and Methodological Soundness. A critical innovation is our empirically grounded weighting scheme for these dimensions, optimized on a targeted subset of the S2ORC corpus to reflect performance in cinematic AI workflows. Applied to a curated corpus of 129 publications, the IMPSF identifies and ranks the most significant contributions. Our analysis reveals that AI serves primarily as a powerful augmentative tool, with substantial advancements in each domain driven by technologies like diffusion models, large language models (LLMs), and generative adversarial networks (GANs). This review not only synthesizes the current state of the art but also provides a structured taxonomy, a research roadmap, and a foundational framework for future innovation at the intersection of computational creativity and cinematic artistry.<\/p>\n\n\n\n Ref: https:\/\/doi.org\/10.1016\/j.entcom.2025.101076<\/a><\/p>\n\n\n\n\n\n\n
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