DDH-QA: A Dynamic Digital Humans Quality Assessment Database
2022
Online
report
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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DDH-QA: A Dynamic Digital Humans Quality Assessment Database
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Autor/in / Beteiligte Person: | Zhang, Zicheng ; Zhou, Yingjie ; Sun, Wei ; Lu, Wei ; Min, Xiongkuo ; Wang, Yu ; Zhai, Guangtao |
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Veröffentlichung: | 2022 |
Medientyp: | report |
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