Dynamic Digital Humans (DDHs) are 3D digital models that are animated using predefined motions and are inevitably bothered by noise/shift during the generation process and compression distortion during the transmission process, which needs to be perceptually evaluated. Usually, DDHs are displayed as 2D rendered animation videos and it is natural to adapt video quality assessment (VQA) methods to DDH quality assessment (DDH-QA) tasks. However, the VQA methods are highly dependent on viewpoints and less sensitive to geometry-based distortions. Therefore, in this paper, we propose a novel no-reference (NR) geometry-aware video quality assessment method for DDH-QA challenge. Geometry characteristics are described by the statistical parameters estimated from the DDHs' geometry attribute distributions. Spatial and temporal features are acquired from the rendered videos. Finally, all kinds of features are integrated and regressed into quality values. Experimental results show that the proposed method achieves state-of-the-art performance on the DDH-QA database.

翻译：动态数字人类是使用预定义动作进行动画化的三维数字模型，其在生成过程中不可避免地受到噪声/偏移干扰，在传输过程中受到压缩失真影响，因此需要进行感知质量评估。通常，动态数字人类以二维渲染动画视频的形式呈现，因此自然可以调整视频质量评估方法用于动态数字人类质量评估任务。然而，视频质量评估方法高度依赖视角，且对基于几何的失真不敏感。为此，本文针对动态数字人类质量评估挑战，提出了一种新颖的无参考几何感知视频质量评估方法。通过从动态数字人类的几何属性分布中估计统计参数来描述几何特征，从渲染视频中提取时空特征，最终将各类特征集成并回归为质量值。实验结果表明，该方法在动态数字人类质量评估数据库上达到了最先进的性能。