DDH-QA: A Dynamic Digital Humans Quality Assessment Database
- URL: http://arxiv.org/abs/2212.12734v3
- Date: Mon, 28 Aug 2023 07:58:48 GMT
- Title: DDH-QA: A Dynamic Digital Humans Quality Assessment Database
- Authors: Zicheng Zhang, Yingjie Zhou, Wei Sun, Wei Lu, Xiongkuo Min, Yu Wang,
and Guangtao Zhai
- Abstract summary: We construct a large-scale dynamic digital human quality assessment database with diverse motion content as well as multiple distortions.
Ten types of common motion are employed to drive the DDHs and a total of 800 DDHs are generated in the end.
- Score: 55.69700918818879
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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