用大模型评估AI生成视频质量，从技术、动作到语义多维度打分。
Towards Holistic Visual Quality Assessment of AI-Generated Videos: A LLM-Based Multi-Dimensional Evaluation Model
- 分技术、运动、语义三维度建模，用大模型融合多模态特征。
- 在NTIRE 2025挑战赛中获第二名，量化评估效果优异。
- 适合关注AI视频质量评测与模型优化的研究者使用。
近年来，AI生成视频（AIGV）技术发展迅速，深刻改变了视频内容生产范式。然而，当前AIGV仍存在噪声、模糊、帧抖动和动态度低等显著视觉质量问题，严重影响用户体验。因此，有效的自动视觉质量评估对内容监管与生成模型优化至关重要。本文将AIGV视觉质量分解为技术质量、运动质量与视频语义三个维度，为每个维度设计专用编码器以实现有效特征表示。考虑到大语言模型（LLMs）在多模态任务中的卓越表现，本文引入LLM作为质量回归模块，并设计专门的多模态提示工程框架，增强模型对多维特征与视觉质量间推理关联的理解。训练阶段采用LoRA微调技术，使LLM更适应特定任务。所提方法在NTIRE 2025 AI生成内容质量评估挑战赛-视频赛道中取得第二名，验证了其有效性。代码已开源：https://github.com/QiZelu/AIGVEval。
原文摘要 · Abstract (English)
The development of AI-Generated Video (AIGV) technology has been remarkable in recent years, significantly transforming the paradigm of video content production. However, AIGVs still suffer from noticeable visual quality defects, such as noise, blurriness, frame jitter and low dynamic degree, which severely impact the user's viewing experience. Therefore, an effective automatic visual quality assessment is of great importance for AIGV content regulation and generative model improvement. In this work, we decompose the visual quality of AIGVs into three dimensions: technical quality, motion quality, and video semantics. For each dimension, we design corresponding encoder to achieve effective feature representation. Moreover, considering the outstanding performance of large language models (LLMs) in various vision and language tasks, we introduce a LLM as the quality regression module. To better enable the LLM to establish reasoning associations between multi-dimensional features and visual quality, we propose a specially designed multi-modal prompt engineering framework. Additionally, we incorporate LoRA fine-tuning technology during the training phase, allowing the LLM to better adapt to specific tasks. Our proposed method achieved \textbf{second place} in the NTIRE 2025 Quality Assessment of AI-Generated Content Challenge: Track 2 AI Generated video, demonstrating its effectiveness. Codes can be obtained at https://github.com/QiZelu/AIGVEval.
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