GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
<|reference_start|>GAIA: Rethinking Action Quality Assessment for AI-Generated Videos: Assessing action quality is both imperative and challenging due to its significant impact on the quality of AI-generated videos, further complicated by the inherently ambiguous nature of actions within AI-generated video (AIGV). Current action quality assessment (AQA) algorithms predominantly focus on actions from real specific scenarios and are pre-trained with normative action features, thus rendering them inapplicable in AIGVs. To address these problems, we construct GAIA, a Generic AI-generated Action dataset, by conducting a large-scale subjective evaluation from a novel causal reasoning-based perspective, resulting in 971,244 ratings among 9,180 video-action pairs. Based on GAIA, we evaluate a suite of popular text-to-video (T2V) models on their ability to generate visually rational actions, revealing their pros and cons on different categories of actions. We also extend GAIA as a testbed to benchmark the AQA capacity of existing automatic evaluation methods. Results show that traditional AQA methods, action-related metrics in recent T2V benchmarks, and mainstream video quality methods correlate poorly with human opinions, indicating a sizable gap between current models and human action perception patterns in AIGVs. Our findings underscore the significance of action quality as a unique perspective for studying AIGVs and can catalyze progress towards methods with enhanced capacities for AQA in AIGVs.<|reference_end|>
@article{chen2024gaia:,
    title={GAIA: Rethinking Action Quality Assessment for AI-Generated Videos},
    author={Zijian Chen, Wei Sun, Yuan Tian, Jun Jia, Zicheng Zhang, Jiarui Wang,
  Ru Huang, Xiongkuo Min, Guangtao Zhai, Wenjun Zhang},
    journal={arXiv preprint arXiv:2406.06087},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06087},
    primaryClass={cs.CV}
}