Digital humans have witnessed extensive applications in various domains, necessitating related quality assessment studies. However, there is a lack of comprehensive digital human quality assessment (DHQA) databases. To address this gap, we propose SJTU-H3D, a subjective quality assessment database specifically designed for full-body digital humans. It comprises 40 high-quality reference digital humans and 1,120 labeled distorted counterparts generated with seven types of distortions. The SJTU-H3D database can serve as a benchmark for DHQA research, allowing evaluation and refinement of processing algorithms. Further, we propose a zero-shot DHQA approach that focuses on no-reference (NR) scenarios to ensure generalization capabilities while mitigating database bias. Our method leverages semantic and distortion features extracted from projections, as well as geometry features derived from the mesh structure of digital humans. Specifically, we employ the Contrastive Language-Image Pre-training (CLIP) model to measure semantic affinity and incorporate the Naturalness Image Quality Evaluator (NIQE) model to capture low-level distortion information. Additionally, we utilize dihedral angles as geometry descriptors to extract mesh features. By aggregating these measures, we introduce the Digital Human Quality Index (DHQI), which demonstrates significant improvements in zero-shot performance. The DHQI can also serve as a robust baseline for DHQA tasks, facilitating advancements in the field. The database and the code are available at https://github.com/zzc-1998/SJTU-H3D.

翻译：数字人在各个领域得到了广泛应用，这促使了相关质量评估研究的必要。然而，目前尚缺乏综合性的数字人质量评估（DHQA）数据库。为填补这一空白，我们提出了SJTU-H3D——一个专门面向全身数字人的主观质量评估数据库。该数据库包含40个高质量参考数字人及其经七种失真类型生成的1120个标注失真样本。SJTU-H3D数据库可作为DHQA研究的基准，支持处理算法的评估与优化。进一步地，我们提出了一种零样本DHQA方法，聚焦无参考（NR）场景，以确保泛化能力并缓解数据库偏差。该方法利用从投影中提取的语义与失真特征，以及从数字人网格结构中获取的几何特征。具体而言，我们采用对比语言-图像预训练（CLIP）模型度量语义亲和度，并引入自然图像质量评估（NIQE）模型捕获低级失真信息。此外，我们使用二面角作为几何描述子提取网格特征。通过聚合这些度量，我们提出了数字人质量指数（DHQI），该指数在零样本性能上展现出显著提升。DHQI也可作为DHQA任务的稳健基线，推动该领域的发展。数据库及代码开源地址为：https://github.com/zzc-1998/SJTU-H3D。