  In recent years, artificial intelligence (AI) driven video generation has
garnered significant attention due to advancements in stable diffusion and
large language model techniques. Thus, there is a great demand for accurate
video quality assessment (VQA) models to measure the perceptual quality of
AI-generated content (AIGC) videos as well as optimize video generation
techniques. However, assessing the quality of AIGC videos is quite challenging
due to the highly complex distortions they exhibit (e.g., unnatural action,
irrational objects, etc.). Therefore, in this paper, we try to systemically
investigate the AIGC-VQA problem from both subjective and objective quality
assessment perspectives. For the subjective perspective, we construct a
Large-scale Generated Vdeo Quality assessment (LGVQ) dataset, consisting of
2,808 AIGC videos generated by 6 video generation models using 468 carefully
selected text prompts. Unlike previous subjective VQA experiments, we evaluate
the perceptual quality of AIGC videos from three dimensions: spatial quality,
temporal quality, and text-to-video alignment, which hold utmost importance for
current video generation techniques. For the objective perspective, we
establish a benchmark for evaluating existing quality assessment metrics on the
LGVQ dataset, which reveals that current metrics perform poorly on the LGVQ
dataset. Thus, we propose a Unify Generated Video Quality assessment (UGVQ)
model to comprehensively and accurately evaluate the quality of AIGC videos
across three aspects using a unified model, which uses visual, textual and
motion features of video and corresponding prompt, and integrates key features
to enhance feature expression. We hope that our benchmark can promote the
development of quality evaluation metrics for AIGC videos. The LGVQ dataset and
the UGVQ metric will be publicly released.
