VideoScore2: Think before You Score in Generative Video Evaluation
Authors: name: Xuan He; name: Dongfu Jiang; name: Ping Nie; name: Minghao Liu; name: Zhengxuan Jiang; name: Mingyi Su; name: Wentao Ma; name: Junru Lin; name: Chun Ye; name: Yi Lu; name: Keming Wu; name: Benjamin Schneider; name: Quy Duc Do; name: Zhuofeng Li; name: Yiming Jia; name: Yuxuan Zhang; name: Guo Cheng; name: Haozhe Wang; name: Wangchunshu Zhou; name: Qunshu Lin; name: Yuanxing Zhang; name: Ge Zhang; name: Wenhao Huang; name: Wenhu Chen
Published: 2025-09-26 18:09:03

Abstract: Recent advances in text-to-video generation have produced increasingly
realistic and diverse content, yet evaluating such videos remains a fundamental
challenge due to their multi-faceted nature encompassing visual quality,
semantic alignment, and physical consistency. Existing evaluators and reward
models are limited to single opaque scores, lack interpretability, or provide
only coarse analysis, making them insufficient for capturing the comprehensive
nature of video quality assessment. We present VideoScore2, a
multi-dimensional, interpretable, and human-aligned framework that explicitly
evaluates visual quality, text-to-video alignment, and physical/common-sense
consistency while producing detailed chain-of-thought rationales. Our model is
trained on a large-scale dataset VideoFeedback2 containing 27,168
human-annotated videos with both scores and reasoning traces across three
dimensions, using a two-stage pipeline of supervised fine-tuning followed by
reinforcement learning with Group Relative Policy Optimization (GRPO) to
enhance analytical robustness. Extensive experiments demonstrate that
VideoScore2 achieves superior performance with 44.35 (+5.94) accuracy on our
in-domain benchmark VideoScore-Bench-v2 and 50.37 (+4.32) average performance
across four out-of-domain benchmarks (VideoGenReward-Bench, VideoPhy2, etc),
while providing interpretable assessments that bridge the gap between
evaluation and controllable generation through effective reward modeling for
Best-of-N sampling. Project Page: https://tiger-ai-lab.github.io/VideoScore2/