  In recent years, digital humans have been widely applied in augmented/virtual
reality (A/VR), where viewers are allowed to freely observe and interact with
the volumetric content. However, the digital humans may be degraded with
various distortions during the procedure of generation and transmission.
Moreover, little effort has been put into the perceptual quality assessment of
digital humans. Therefore, it is urgent to carry out objective quality
assessment methods to tackle the challenge of digital human quality assessment
(DHQA). In this paper, we develop a novel no-reference (NR) method based on
Transformer to deal with DHQA in a multi-task manner. Specifically, the front
2D projections of the digital humans are rendered as inputs and the vision
transformer (ViT) is employed for the feature extraction. Then we design a
multi-task module to jointly classify the distortion types and predict the
perceptual quality levels of digital humans. The experimental results show that
the proposed method well correlates with the subjective ratings and outperforms
the state-of-the-art quality assessment methods.
