Problem
Research questions and friction points this paper is trying to address.
camera-controlled
perceptual quality assessment
video generation
CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation
🤖 AI Summary
研究针对相机控制生成视频的感知质量评估问题，提出CamWorldQA基准及CWQA网络，通过提取空间、时间运动和光流特征预测质量分数。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
camera-controlled world video generation
perceptual quality assessment
no-reference quality assessment network
spatial-temporal features
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