mlsanityData to 7 Oct 2026

Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap

arXiv 2404.13573 ↗First posted 21 Apr 2024cs.CVcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 39 of its 51 references that are in the map. First posted on arXiv 21 Apr 2024.

Builds on

39 of 51 references.

References · 39

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27 more
  1. 2023EvalCrafter: Benchmarking and Evaluating Large Video Generation Modelscited by 332
  2. 2022Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectivescited by 314
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 3.

13 papers.

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  1. 2025VideoGen-Eval: Agent-based System for Video Generation Evaluationcited by 23
  2. 2024NTIRE 2024 Quality Assessment of AI-Generated Content Challengecited by 20
  3. 2024PPLLaVA: Varied Video Sequence Understanding With Prompt Guidancecited by 20
  4. 2024VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessmentcited by 10
  5. 2025Flow4Agent: Long-form Video Understanding via Motion Prior from Optical Flowcited by 9
  6. 2025GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoningcited by 7
  7. 2024Few-Shot Image Quality Assessment via Adaptation of Vision-Language Modelscited by 4
  8. 2025VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videoscited by 4
  9. 2025Multi-Dimensional Quality Assessment for Text-to-3D Assets: Dataset and Modelcited by 2
  10. 2026Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Videocited by 2
  11. 2025IE-Critic-R1: Advancing the Explanatory Measurement of Text-Driven Image Editing for Human Perception Alignmentcited by 1
  12. 2026Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluationcited by 1
1 more
  1. 2026Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessmentcited by 0