← 研踪 PaperTrailarXiv:2611.07377 · v1
Designing with Cinematic Language: An Intent-Driven Approach to Human-AI Video Clip Co-Creation
首次提交 2026-11-05 11:31
版本更新 2026-11-05 11:31
雷达观测 2026-11-06 10:04
摘要
Generative video models have made video creation more accessible, yet creators still struggle to translate high-level narrative intentions into coherent cinematic expressions. Existing systems primarily rely on prompt refinement or expose isolated camera controls, leaving a gap between what to express and how to film it. We present CineAssist, a human-AI co-creation system that treats cinematic language as an interactive design medium connecting creative intentions with executable video controls. We construct a Cinematic Language Design Space and an annotated dataset from 107 filmmaking sources and 1,436 film clips, linking communicative intentions to cinematic parameter values. CineAssist combines a multi-agent workflow for intent alignment and strategy recommendation with an interactive cinematic editor and a context-sensitive preference learner for user-controlled personalization. We evaluate the system through three studies examining end-to-end usability, cinematic quality, and preference evolution. Results show that CineAssist facilitates efficient, controllable, and personalized intent-driven cinematic video creation.
作者备注：22 pages, 16 figures
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v1 · 2026-11-05 11:31 · 最新观测版本
Designing with Cinematic Language: An Intent-Driven Approach to Human-AI Video Clip Co-Creation
Generative video models have made video creation more accessible, yet creators still struggle to translate high-level narrative intentions into coherent cinematic expressions. Existing systems primarily rely on prompt refinement or expose isolated camera controls, leaving a gap between what to express and how to film it. We present CineAssist, a human-AI co-creation system that treats cinematic language as an interactive design medium connecting creative intentions with executable video controls. We construct a Cinematic Language Design Space and an annotated dataset from 107 filmmaking sources and 1,436 film clips, linking communicative intentions to cinematic parameter values. CineAssist combines a multi-agent workflow for intent alignment and strategy recommendation with an interactive cinematic editor and a context-sensitive preference learner for user-controlled personalization. We evaluate the system through three studies examining end-to-end usability, cinematic quality, and preference evolution. Results show that CineAssist facilitates efficient, controllable, and personalized intent-driven cinematic video creation.
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