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Related Experiment Video

Updated: Sep 11, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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A Perspective on Quality Evaluation for AI-Generated Videos.

Zhichao Zhang1, Wei Sun1, Guangtao Zhai1

  • 1Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.

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|August 14, 2025
PubMed
Summary
This summary is machine-generated.

Multimodal large language models (MLLMs) offer a new approach to evaluating AI-generated videos. These models assess video quality by integrating multiple data types, improving upon existing methods.

Keywords:
AI-generated videoMLLMvideo quality assessment

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Multimedia Processing

Background:

  • AI-generated content (AIGC) has advanced video creation, but reliable quality evaluation is challenging.
  • Current methods struggle with spatial fidelity, temporal coherence, and semantic alignment in machine-crafted videos.
  • Sensor technologies are crucial for ensuring the physical plausibility of AIGC outputs.

Purpose of the Study:

  • To propose multimodal large language models (MLLMs) as a cornerstone for next-generation video quality assessment (VQA).
  • To highlight the potential of MLLMs in overcoming limitations of traditional VQA methods.
  • To analyze current AIGC video quality assessment methodologies.

Main Methods:

  • Utilizing MLLMs to jointly encode multi-modal cues (vision, language, sound, depth).
  • Leveraging MLLM's language understanding for assessing scene composition, motion dynamics, and narrative consistency.
  • Analyzing existing AIGC generation models, datasets, quality dimensions, and evaluation frameworks.

Main Results:

  • MLLMs can overcome the fragmentation of hand-engineered metrics and poor generalization of CNN-based methods.
  • Advances in sensor fusion enable MLLMs to integrate physical constraints with semantic interpretations.
  • Enhanced accuracy in visual quality assessment through combined low-level and high-level feature analysis.

Conclusions:

  • MLLMs represent a significant advancement for robust and comprehensive AIGC video quality assessment.
  • The integration of multi-modal data and sensor fusion is key to future VQA systems.
  • Future research should focus on developing and refining MLLM-based VQA frameworks.