A Perspective on Quality Evaluation for AI-Generated Videos
Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fideli...
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MDPI AG
2025-07-01
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| author | Zhichao Zhang Wei Sun Guangtao Zhai |
| author_facet | Zhichao Zhang Wei Sun Guangtao Zhai |
| author_sort | Zhichao Zhang |
| collection | DOAJ |
| description | Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment. |
| format | Article |
| id | doaj-art-7ca1b10aaa0e48b0bbb7ea2b45c8ee01 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-7ca1b10aaa0e48b0bbb7ea2b45c8ee012025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-07-012515466810.3390/s25154668A Perspective on Quality Evaluation for AI-Generated VideosZhichao Zhang0Wei Sun1Guangtao Zhai2Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200030, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200030, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200030, ChinaRecent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment.https://www.mdpi.com/1424-8220/25/15/4668video quality assessmentAI-generated videoMLLM |
| spellingShingle | Zhichao Zhang Wei Sun Guangtao Zhai A Perspective on Quality Evaluation for AI-Generated Videos Sensors video quality assessment AI-generated video MLLM |
| title | A Perspective on Quality Evaluation for AI-Generated Videos |
| title_full | A Perspective on Quality Evaluation for AI-Generated Videos |
| title_fullStr | A Perspective on Quality Evaluation for AI-Generated Videos |
| title_full_unstemmed | A Perspective on Quality Evaluation for AI-Generated Videos |
| title_short | A Perspective on Quality Evaluation for AI-Generated Videos |
| title_sort | perspective on quality evaluation for ai generated videos |
| topic | video quality assessment AI-generated video MLLM |
| url | https://www.mdpi.com/1424-8220/25/15/4668 |
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