VideoScore2: Think before You Score in Generative Video Evaluation
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/
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Xuan He
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Xuan He
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Dongfu Jiang
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Dongfu Jiang
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Ping Nie
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Ping Nie
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Minghao Liu
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minghao
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Zhengxuan Jiang
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Mingyi Su
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Wentao Ma
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Junru Lin
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Chun Ye
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Daisy Ye
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Yi Lu
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Tom Lu
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Keming Wu
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Keming Wu
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Benjamin Schneider
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Quy Duc Do
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Zhuofeng Li
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Yuxuan Zhang
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Yuxuan Zhang
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Guo Cheng
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Haozhe Wang
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Wangchunshu Zhou
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Yuanxing Zhang
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Wenhao Huang
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Wenhu Chen
VideoScore2: Think before You Score in Generative Video Evaluation
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/
VideoScore2 is a multi-dimensional, interpretable framework for evaluating text-to-video generation, assessing visual quality, alignment, and consistency with detailed rationales.
text-to-video generation visual quality semantic alignment physical consistency VideoScore2 VideoFeedback2 supervised fine-tuning reinforcement learning Group Relative Policy Optimization (GRPO) VideoScore-Bench-v2 VideoGenReward-Bench VideoPhy2 Best-of-N sampling