Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model

Understand

In recent years, artificial intelligence (AI)-driven video generation has gained significant attention.

• Consequently, there is a growing need for accurate video quality assessment (VQA) metrics to evaluate the perceptual quality of AI-generated content (AIGC) videos and optimize video generation models.
• However, assessing the quality of AIGC videos remains a significant challenge because these videos often exhibit highly complex distortions, such as unnatural actions and irrational objects.
• To address this challenge, we systematically investigate the AIGC-VQA problem, considering both subjective and objective quality assessment perspectives.