A no-reference quality assessment metric for dynamic 3D digital human

Abstract

Dynamic Digital Humans (DDHs) refer to 3D digital models that are animated using predefined motions. However, these models are susceptible to noise and shift distortions during the generation process, as well as compression distortions during transmission and communication. It is crucial to evaluate the perceptual quality of DDHs in order to address these challenges. Typically, DDHs are presented as 2D rendered animation videos, making it reasonable to adapt video quality assessment (VQA) methods for DDH quality assessment (DDH-QA) tasks. Nevertheless, VQA methods heavily rely on viewpoints and exhibit limited sensitivity to geometry-based distortions. Consequently, this paper introduces a novel no-reference (NR) quality assessment metric specifically tailored for the DDH-QA challenge. In this proposed method, the geometry characteristics of DDHs are characterized by computing statistical parameters estimated from the distributions of their geometry attributes. Additionally, spatial and temporal features are extracted from the rendered videos. Subsequently, all relevant features are integrated and regressed into quality values. Through comprehensive experiments conducted on the DDH-QA database, the results demonstrate that the proposed method achieves state-of-the-art performance in assessing the quality of DDHs.