  In recent years, large amounts of effort have been put into pushing forward
the real-world application of dynamic digital human (DDH). However, most
current quality assessment research focuses on evaluating static 3D models and
usually ignores motion distortions. Therefore, in this paper, we construct a
large-scale dynamic digital human quality assessment (DDH-QA) database with
diverse motion content as well as multiple distortions to comprehensively study
the perceptual quality of DDHs. Both model-based distortion (noise,
compression) and motion-based distortion (binding error, motion unnaturalness)
are taken into consideration. Ten types of common motion are employed to drive
the DDHs and a total of 800 DDHs are generated in the end. Afterward, we render
the video sequences of the distorted DDHs as the evaluation media and carry out
a well-controlled subjective experiment. Then a benchmark experiment is
conducted with the state-of-the-art video quality assessment (VQA) methods and
the experimental results show that existing VQA methods are limited in
assessing the perceptual loss of DDHs.
