Hi3DEval：通过层次化有效性推进3D生成评估

Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity

August 7, 2025

作者: Yuhan Zhang, Long Zhuo, Ziyang Chu, Tong Wu, Zhibing Li, Liang Pan, Dahua Lin, Ziwei Liu

cs.AI

摘要

尽管3D内容生成技术发展迅速，但针对生成3D资产的质量评估仍面临挑战。现有方法主要依赖基于图像的度量标准，且仅在对象层面进行操作，限制了其捕捉空间一致性、材质真实感及高保真局部细节的能力。1) 为解决这些问题，我们推出了Hi3DEval，一个专为3D生成内容设计的层次化评估框架。该框架结合了对象级与部件级评估，实现了跨多维度全面评估及细粒度质量分析。此外，我们扩展了纹理评估范畴，不仅关注美学外观，还特别强调材质真实感的评估，聚焦于反照率、饱和度及金属质感等属性。2) 为支撑此框架，我们构建了Hi3DBench，一个包含多样化3D资产及高质量标注的大规模数据集，并配备了一套可靠的多代理标注流程。我们进一步提出了一种基于混合3D表示的3D感知自动评分系统。具体而言，我们利用基于视频的表示进行对象级和材质主题评估，以增强时空一致性的建模，并采用预训练的3D特征进行部件级感知。大量实验表明，我们的方法在建模3D特性上优于现有基于图像的度量标准，且与人类偏好高度一致，为手动评估提供了可扩展的替代方案。项目页面详见https://zyh482.github.io/Hi3DEval/。

English

Despite rapid advances in 3D content generation, quality assessment for the generated 3D assets remains challenging. Existing methods mainly rely on image-based metrics and operate solely at the object level, limiting their ability to capture spatial coherence, material authenticity, and high-fidelity local details. 1) To address these challenges, we introduce Hi3DEval, a hierarchical evaluation framework tailored for 3D generative content. It combines both object-level and part-level evaluation, enabling holistic assessments across multiple dimensions as well as fine-grained quality analysis. Additionally, we extend texture evaluation beyond aesthetic appearance by explicitly assessing material realism, focusing on attributes such as albedo, saturation, and metallicness. 2) To support this framework, we construct Hi3DBench, a large-scale dataset comprising diverse 3D assets and high-quality annotations, accompanied by a reliable multi-agent annotation pipeline. We further propose a 3D-aware automated scoring system based on hybrid 3D representations. Specifically, we leverage video-based representations for object-level and material-subject evaluations to enhance modeling of spatio-temporal consistency and employ pretrained 3D features for part-level perception. Extensive experiments demonstrate that our approach outperforms existing image-based metrics in modeling 3D characteristics and achieves superior alignment with human preference, providing a scalable alternative to manual evaluations. The project page is available at https://zyh482.github.io/Hi3DEval/.