Q-Save: Towards Scoring and Attribution for Generated Video Evaluation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914355065389056 |
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| author | Wu, Xiele Zhang, Zicheng Chen, Mingtao Liu, Yixian Liu, Yiming Wang, Shushi Hu, Zhichao Liu, Yuhong Zhai, Guangtao Liu, Xiaohong |
| author_facet | Wu, Xiele Zhang, Zicheng Chen, Mingtao Liu, Yixian Liu, Yiming Wang, Shushi Hu, Zhichao Liu, Yuhong Zhai, Guangtao Liu, Xiaohong |
| contents | Evaluating AI-generated video (AIGV) quality hinges on three crucial dimensions: visual quality, dynamic quality, and text-video alignment. While numerous evaluation datasets and algorithms have been proposed, existing approaches are constrained by two limitations: the absence of systematic definitions for evaluation dimensions, and the isolated treatment of the three dimensions in separate models. Therefore, we introduce Q-Save, a holistic benchmark dataset and unified evaluation model for AIGV quality assessment. The Q-Save dataset contains nearly 10,000 video samples, each annotated with Mean Opinion Scores (MOS) and fine-grained attribution explanations across the three core dimensions. Leveraging this attribution-annotated dataset, we train the proposed Q-Save model, which adopts the SlowFast framework to balance accuracy and efficiency, and employs a three-stage training strategy with Chain-of-Thought (COT) formatted data: Supervised Fine-Tuning (SFT), Grouped Relative Policy Optimization (GRPO), and a final SFT round for stability, to jointly perform quality scoring and attribution generation. Experimental results demonstrate that Q-Save achieves superior performance in AIGV quality prediction while providing interpretable justifications. Code and dataset will be released upon publication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Q-Save: Towards Scoring and Attribution for Generated Video Evaluation Wu, Xiele Zhang, Zicheng Chen, Mingtao Liu, Yixian Liu, Yiming Wang, Shushi Hu, Zhichao Liu, Yuhong Zhai, Guangtao Liu, Xiaohong Computer Vision and Pattern Recognition Evaluating AI-generated video (AIGV) quality hinges on three crucial dimensions: visual quality, dynamic quality, and text-video alignment. While numerous evaluation datasets and algorithms have been proposed, existing approaches are constrained by two limitations: the absence of systematic definitions for evaluation dimensions, and the isolated treatment of the three dimensions in separate models. Therefore, we introduce Q-Save, a holistic benchmark dataset and unified evaluation model for AIGV quality assessment. The Q-Save dataset contains nearly 10,000 video samples, each annotated with Mean Opinion Scores (MOS) and fine-grained attribution explanations across the three core dimensions. Leveraging this attribution-annotated dataset, we train the proposed Q-Save model, which adopts the SlowFast framework to balance accuracy and efficiency, and employs a three-stage training strategy with Chain-of-Thought (COT) formatted data: Supervised Fine-Tuning (SFT), Grouped Relative Policy Optimization (GRPO), and a final SFT round for stability, to jointly perform quality scoring and attribution generation. Experimental results demonstrate that Q-Save achieves superior performance in AIGV quality prediction while providing interpretable justifications. Code and dataset will be released upon publication. |
| title | Q-Save: Towards Scoring and Attribution for Generated Video Evaluation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18825 |