The recent years have witnessed great advances in video generation. However,
the development of automatic video metrics is lagging significantly behind.
None of the existing metric is able to provide reliable scores over generated
videos. The main barrier is the lack of large-scale human-annotated dataset. In
this paper, we release VideoFeedback, the first large-scale dataset containing
human-provided multi-aspect score over 37.6K synthesized videos from 11
existing video generative models. We train MantisScore (initialized from
Mantis) based on VideoFeedback to enable automatic video quality assessment.
Experiments show that the Spearman correlation between MantisScore and humans
can reach 77.1 on VideoFeedback-test, beating the prior best metrics by about
50 points. Further result on other held-out EvalCrafter, GenAI-Bench, and
VBench show that MantisScore has consistently much higher correlation with
human judges than other metrics. Due to these results, we believe MantisScore
can serve as a great proxy for human raters to (1) rate different video models
to track progress (2) simulate fine-grained human feedback in Reinforcement
Learning with Human Feedback (RLHF) to improve current video generation models.

本文介绍了一个基于 VideoFeedback 数据集的 MantisScore 方法，该方法在自动生成的视频质量评估方面表现出较高的相关性，可作为人类评级者的一个良好代理，用于跟踪不同视频模型的进展，并在强化学习等领域中模拟精细的人类反馈以改善当前的视频生成模型。