  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.
