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

Geometry-Aware Video Quality Assessment for Dynamic Digital Human