MantisScore: Building Automatic Metrics to Simulate Fine-grained Human
  Feedback for Video Generation
Authors: name: Xuan He, name: Dongfu Jiang, name: Ge Zhang, name: Max Ku, name: Achint Soni, name: Sherman Siu, name: Haonan Chen, name: Abhranil Chandra, name: Ziyan Jiang, name: Aaran Arulraj, name: Kai Wang, name: Quy Duc Do, name: Yuansheng Ni, name: Bohan Lyu, name: Yaswanth Narsupalli, name: Rongqi Fan, name: Zhiheng Lyu, name: Yuchen Lin, name: Wenhu Chen

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.