CamWorldQA：相机控制型世界视频生成的感知质量评估

中文摘要 AI 辅助

生成式视频模型的最新进展已实现相机控制型世界视频生成，使模型能在用户定义的相机轨迹下合成视频。然而，现有视频质量评估（VQA）方法主要针对自然视频开发，无法捕捉相机控制型生成的独特感知特性，如视点一致性、运动连贯性和内容保留。本研究中，我们推出CamWorldQA，首个用于相机控制型世界视频生成感知质量评估的基准。CamWorldQA包含720个由6种代表性生成方法在20种不同源视频及6种相机轨迹下生成的视频，每个视频均通过主观实验标注了人类评定的感知质量分数。此外，我们提出CWQA，一种无参考质量评估网络，具备三个互补分支，分别提取空间特征、时间运动特征和光流特征以联合预测质量分数。大量实验表明，CWQA在CamWorldQA数据集上的性能优于现有质量评估方法。

英文摘要

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.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• Eindhoven University of Technology（埃因霍温理工大学）

机构由 AI 辅助整理，请以论文原文为准。