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.

翻译：近年来，大量研究致力于推动动态数字人在实际应用中的发展。然而，当前大多数质量评估研究聚焦于静态3D模型评估，通常忽略运动失真。因此，本文构建了一个包含多样化运动内容与多重失真的大规模动态数字人质量评估数据库，旨在全面研究动态数字人的感知质量。该数据库同时考虑了基于模型的失真（噪声、压缩）与基于运动的失真（绑定错误、运动不自然）。采用十种常见运动类型驱动动态数字人，最终生成800个动态数字人样本。随后，我们以失真动态数字人的视频序列作为评估媒介，开展了严格受控的主观实验。进一步基于当前最先进的视频质量评估方法进行基准实验，结果表明现有视频质量评估方法在评估动态数字人的感知损失方面存在局限性。