GazeEval：用眼动追踪数据预测 AI 生成视频的视觉真实感
Evaluating Visual Realism of AI-Generated Videos with Eye Tracking
90人看了438个AI视频，靠眼动数据就能71.3%准确率预测观众觉得真假，做视频生成评估的可以看看这套方法。
一篇 arXiv 论文提出用眼动追踪作为 AI 生成视频的人评补充信号。研究开展了两次 Visual Turing Test（VTT）实验，共 90 名参与者、438 个视频，收集到 4,286 条有效眼动试验数据。基于这些数据，作者提出首个计算方法 GazeEval，从眼动数据预测观众感知的视觉真实感，准确率达到 71.3%。研究数据已公开，支持眼动评估方向的后续研究。
Evaluating Visual Realism of AI-Generated Videos with Eye Tracking
Human evaluation of AI-generated videos has so far relied on explicit human feedback, such as judgments or ratings. While these signals capture the conclusions viewers reach, they fail to offer insights into the perception that leads to them. This paper investigates eye tracking as a complementary human evaluation signal for AI-generated videos. We conducted two Visual Turing Test (VTT) experiments with a total of 90 participants on videos generated by latest generative models, in which participants watched each video twice and then gave their feedback. Across the experiments, we collected 4,286 valid eye-tracking trials on 438 videos. Our analysis indicates that gaze patterns offer informative evaluation signals that align with explicit human feedback. Building on the findings, we propose the first computational method GazeEval to predict the perceived visual realism of AI-generated videos from eye-tracking data. It achieves 71.3% accuracy in prediction, providing evidence that eye tracking data carries informative signals about how people evaluate a video. We release the study data to support further research on eye tracking for evaluating AI-generated video.