CamPilot: A Multi-Agent Cinematic Assistant for Camera-Controlled Movie Generation

2026-09-09

AI Summary

为解决AI生成视频中电影语言不精细和多镜头连贯性差的问题，CamPilot采用基于GRPO的学习框架，从14K专业电影中学习摄像技巧与镜头间关系，以生成更高质量的电影。

Abstract

The integration of large language models (LLMs) into video generation has enabled rapid text-to-video creation and improved visual quality. However, it still falls short of professional filmmaking, where cinematographic language is less refined than human-crafted camera work and multi-shot continuity remains challenging. To address these limitations, we introduce CamPilot, a multi-agent framework that integrates cinematographic planning and camera-work control to produce more coherent, logically structured, and human-aesthetic movies. CamPilot adopts a GRPO-based learning paradigm to learn camera work planning from 14K real-world professional movies, internalizing motion patterns and composition principles that support reasoning over shooting techniques (e.g., camera angle, motion, and focal behavior) and cross-shot relationships for controllable camera-viewpoint generation. Multiple agents further collaborate and evolve to improve overall output quality. To support this work and further studies in this domain, we establish CamEval, a benchmark for evaluating camera work quality and cinematic engagement. Empirical results show that CamPilot outperforms state-of-the-art text-to-movie generation methods on cinematographic control and quality, highlighting the impact of professional camera design on movie generation.

Problem

• large language models
• video generation
• cinematographic language
• multi-shot continuity

Innovation

• Multi-Agent Framework
• Cinematographic Planning
• Camera-Work Control
• GRPO-based Learning
• CamEval