Camera Artist: A Multi-Agent Framework for Cinematic Language Storytelling Video Generation

arXiv cs.AI / 4/13/2026

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Key Points

The paper introduces “Camera Artist,” a multi-agent framework aimed at generating narrative video sequences using explicit cinematic language rather than just script-to-video conversion.
It addresses a key gap in prior multi-agent filmmaking systems by adding mechanisms for narrative progression across adjacent shots to reduce fragmented storytelling.
A dedicated Cinematography Shot Agent performs recursive storyboard generation to improve shot-to-shot continuity and injects cinematic-language cues to make shot designs more expressive.
The authors report that their method outperforms existing baselines on metrics and evaluations related to narrative consistency, dynamic expressiveness, and perceived film quality.
Overall, the work positions agentic video generation as closer to a real filmmaking workflow, combining planning (storyboards) with style/shot control (cinematic language injection).

Abstract

We propose Camera Artist, a multi-agent framework that models a real-world filmmaking workflow to generate narrative videos with explicit cinematic language. While recent multi-agent systems have made substantial progress in automating filmmaking workflows from scripts to videos, they often lack explicit mechanisms to structure narrative progression across adjacent shots and deliberate use of cinematic language, resulting in fragmented storytelling and limited filmic quality. To address this, Camera Artist builds upon established agentic pipelines and introduces a dedicated Cinematography Shot Agent, which integrates recursive storyboard generation to strengthen shot-to-shot narrative continuity and cinematic language injection to produce more expressive, film-oriented shot designs. Extensive quantitative and qualitative results demonstrate that our approach consistently outperforms existing baselines in narrative consistency, dynamic expressiveness, and perceived film quality.