  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.
