How Do Professional Editors Evaluate the Editing Quality of AI-Generated Cinematic Video Ads?

2026-08-25

AI Summary

研究通过专业编辑对AI生成的电影广告评价，提出六维评估框架，指导AI生成广告的编辑意识、人工及自动评估。

Abstract

On social media, we often encounter short-form video ads that employ cinematic editing techniques to evoke an emotional response. While AI tools are beginning to generate such cinematic ads automatically, we lack a fine-grained framework for evaluating these ads. In this paper, we first characterize social media video ad formats and identify cinematic ads as a recurring format in our corpus. We then analyze the duration, shot structure, audio and text elements, and editing techniques of cinematic ads to inform a two-stage generation pipeline in which an LLM first generates a shot plan and a video generation model renders the video. Using this pipeline, we generated 70 cinematic ads for 35 real brands and recruited professional video editors to critique their editing choices. From their critiques, we derive six dimensions of editing quality: narrative progression, audiovisual coordination and sound design, visual composition and graphics, shot-to-shot continuity, message and brand coherence, and temporal rhythm and pacing. We discuss how these dimensions can guide editing-aware generation, human evaluation, and automated evaluation of AI-generated cinematic ads.

Problem

• AI-Generated Cinematic Video Ads
• Editing Quality
• Evaluation Framework

Innovation

• cinematic ads
• evaluation framework
• two-stage generation pipeline
• editing quality dimensions
• professional editors' critique