VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra
Abstract
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 VideoScore (initialized from Mantis)based on VideoFeedback to enable automatic video quality assessment. Experiments show that the Spearman’s correlation betweenVideoScore and humans can reach 77.1 on VideoFeedback-test, beating the prior best metrics by about 50 points. Further result onother held-out EvalCrafter, GenAI-Bench, and VBench show that VideoScore has consistently much higher correlation with humanjudges than other metrics. Due to these results, we believe VideoScore 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.
BibTeX
@inproceedings{he-etal-2024-videoscore,
title = "{V}ideo{S}core: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation",
author = "He, Xuan and
Jiang, Dongfu and
Zhang, Ge and
Ku, Max and
Soni, Achint and
Siu, Sherman and
Chen, Haonan and
Chandra, Abhranil and
Jiang, Ziyan and
Arulraj, Aaran and
Wang, Kai and
Do, Quy Duc and
Ni, Yuansheng and
Lyu, Bohan and
Narsupalli, Yaswanth and
Fan, Rongqi and
Lyu, Zhiheng and
Lin, Bill Yuchen and
Chen, Wenhu",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.127/",
doi = "10.18653/v1/2024.emnlp-main.127",
pages = "2105--2123"
}