首个针对相机控制视频生成的感知质量评估基准，解决传统方法不适用问题。
CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation
- 构建多视角、多轨迹的生成视频数据集，含720个视频和人工评分
- 提出三分支无参考模型CWQA，融合空间、运动与光流特征提升预测精度
- 适用于评估可控镜头视频生成质量，尤其适合研究生成一致性与流畅性
生成式视频模型已能实现用户定义镜头轨迹的场景视频生成，但现有视频质量评估方法主要针对自然视频，难以捕捉相机控制生成特有的感知特性，如视角一致性、运动连贯性和内容保真度。本文提出CamWorldQA，首个针对相机控制世界视频生成的感知质量评估基准，包含由6种代表性生成方法在20个源视频上，沿6条镜头轨迹生成的720个视频，并通过主观实验获得人工感知质量评分。同时，提出无参考质量评估网络CWQA，采用三个互补分支分别提取空间特征、时序运动特征和光流特征，联合预测质量得分。大量实验表明，CWQA在CamWorldQA数据集上性能优于现有方法。
原文摘要 · Abstract (English)
Recent advances in generative video models have enabled camera-controlled world video generation, allowing models to synthesize videos under user-defined camera trajectories. However, existing video quality assessment (VQA) methods are mainly developed for natural videos and fail to capture the unique perceptual characteristics of camera-controlled generation, such as viewpoint consistency, motion coherence, and content preservation. In this work, we introduce CamWorldQA, the first benchmark for perceptual quality assessment of camera-controlled world video generation. CamWorldQA contains 720 generated videos produced by 6 representative generation methods from 20 diverse source videos under 6 camera trajectories, where each video is annotated with a human-rated perceptual quality score through subjective experiments. Furthermore, we propose CWQA, a no-reference quality assessment network with three complementary branches that extract spatial features, temporal motion features and optical flow features to jointly predict quality scores. Extensive experiments demonstrate that CWQA achieves superior performance over existing quality assessment methods on the CamWorldQA dataset.
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