  The recent advancements in Text-to-Video Artificial Intelligence Generated
Content (AIGC) have been remarkable. Compared with traditional videos, the
assessment of AIGC videos encounters various challenges: visual inconsistency
that defy common sense, discrepancies between content and the textual prompt,
and distribution gap between various generative models, etc. Target at these
challenges, in this work, we categorize the assessment of AIGC video quality
into three dimensions: visual harmony, video-text consistency, and domain
distribution gap. For each dimension, we design specific modules to provide a
comprehensive quality assessment of AIGC videos. Furthermore, our research
identifies significant variations in visual quality, fluidity, and style among
videos generated by different text-to-video models. Predicting the source
generative model can make the AIGC video features more discriminative, which
enhances the quality assessment performance. The proposed method was used in
the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated
Content - Track 2 Video, demonstrating its effectiveness. Code will be
available at https://github.com/Coobiw/TriVQA.
