With the rapid development of generative models, Artificial Intelligence-Generated Contents (AIGC) have exponentially increased in daily lives. Among them, Text-to-Video (T2V) generation has received widespread attention. Though many T2V models have been released for generating high perceptual quality videos, there is still lack of a method to evaluate the quality of these videos quantitatively. To solve this issue, we establish the largest-scale Text-to-Video Quality Assessment DataBase (T2VQA-DB) to date. The dataset is composed of 10,000 videos generated by 9 different T2V models. We also conduct a subjective study to obtain each video's corresponding mean opinion score. Based on T2VQA-DB, we propose a novel transformer-based model for subjective-aligned Text-to-Video Quality Assessment (T2VQA). The model extracts features from text-video alignment and video fidelity perspectives, then it leverages the ability of a large language model to give the prediction score. Experimental results show that T2VQA outperforms existing T2V metrics and SOTA video quality assessment models. Quantitative analysis indicates that T2VQA is capable of giving subjective-align predictions, validating its effectiveness. The dataset and code will be released at https://github.com/QMME/T2VQA.

翻译：随着生成式模型的快速发展，人工智能生成内容（AIGC）在日常生活中的应用呈指数级增长。其中，文本生成视频（Text-to-Video，T2V）技术受到广泛关注。尽管已有众多T2V模型能够生成高感知质量的视频，但目前仍缺乏对这类视频质量进行定量评估的方法。为解决这一问题，我们构建了迄今规模最大的文本生成视频质量评估数据库（T2VQA-DB），该数据集包含由9种不同T2V模型生成的10,000个视频。我们还开展了主观实验，为每个视频获取对应的平均意见分数。基于T2VQA-DB，我们提出了一种新颖的基于Transformer的主观对齐文本生成视频质量评估模型（T2VQA）。该模型从文本-视频对齐与视频保真度两个维度提取特征，并借助大语言模型的能力输出预测分数。实验结果表明，T2VQA在性能上超越了现有T2V指标及最先进的视频质量评估模型。定量分析进一步验证了T2VQA能够给出主观对齐的预测，证明了其有效性。数据集与代码将在 https://github.com/QMME/T2VQA 开源。