让创作者主导视频生成，通过智能反馈循环提升叙事连贯性。
Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops
- 创作者驱动迭代生成，每轮由人定方向，AI辅助优化。
- 使用角色化多模态大模型提供观众视角的主观点评。
- 零电影经验学生成功制作10分钟剧情片，验证实用价值。
生成式AI使内容创作更加便捷，但许多AI生成视频缺乏叙事连贯性和创意方向，尤其在长时长下问题更突出。与编程中可通过可靠反馈实现自迭代不同，视频生成需对情节、场景和叙事进行主观评价，自然推动引入人类创意指导。我们提出CHIEF框架，一种以创作者为中心的人机协同视频生成系统，支持其在闭环迭代中持续优化视频。创作者主导每轮生成方向，修订由专用精炼代理处理。反馈由角色化多模态大模型生成，该模型观看生成视频并从观众视角给出主观批评，捕捉自评无法获得的反馈。为验证有效性，我们邀请无影视经验的高中生与大学生，从1分钟短片到完整10分钟剧情短片，完成多阶段创作任务。
原文摘要 · Abstract (English)
Generative AI has made content creation increasingly accessible, but many AI-generated videos lack narrative coherence and creative direction, issues that become more substantial at longer durations. Unlike coding, where AI generation benefits from reliable feedback and techniques such as recurrent self-improvement, video generation requires subjective feedback about plot, scenes, and narrative, which naturally motivates approaches that incorporate human creative direction. We introduce CHIEF, a human-AI co-creation video generation framework that places the creator at the center of human-in-the-loop iterative video refinement, and supports them by providing automatic subjective feedback. The creator incorporates their creative direction by driving each iteration, while their revisions are incorporated by a specialized refiner agent. The feedback loop is generated by persona-conditioned multimodal LLMs that watch generated videos and produce subjective critique from the audience perspectives, providing feedback that self-evaluation alone cannot capture. To test the effectiveness of our proposed framework, we work with high school and college students with no prior filmmaking experience to create videos, from short 1-minute videos to a complete short 10-minute film with a complicated plot.
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