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.

在这篇论文中，我们开发了一种基于 Transformer 的新型无参考（NR）方法，用于以多任务方式处理数字人的感知质量评估（DHQA）。具体而言，我们将数字人的前 2D 投影渲染为输入，并采用视觉 Transformer（ViT）进行特征提取。然后，我们设计了一个多任务模块，共同分类数字人的失真类型和预测感知质量水平。实验结果表明，所提出的方法与主观评级具有良好的相关性，并且优于现有的质量评估方法。