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

2026-04-10

AI Summary

Existing multi-agent video generation approaches often suffer from insufficient narrative coherence across shots and limited utilization of cinematic language, resulting in fragmented storytelling and weak visual expressiveness. To address these limitations, this work proposes a novel multi-agent framework that emulates real-world filmmaking pipelines by explicitly introducing a cinematographic shot agent. Through recursive storyboard generation and explicit modeling of cinematic language, the framework enables end-to-end automated video synthesis from textual scripts. Experimental results demonstrate that the proposed method substantially enhances narrative consistency, dynamic shot expressiveness, and overall perceived cinematic quality, outperforming current state-of-the-art baselines across multiple dimensions.

Technology Category

• Multiagent Systems: Multiagent Planning
• Computer Vision: Computational Photography, Image & Video Synthesis
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• cinematic language
• narrative continuity
• multi-agent framework
• storytelling video generation
• shot-to-shot coherence

Innovation

• multi-agent framework
• cinematic language
• narrative continuity
• recursive storyboard generation
• Cinematography Shot Agent