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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