Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
Authors: name: Yuhan Zhang, name: Long Zhuo, name: Ziyang Chu, name: Tong Wu, name: Zhibing Li, name: Liang Pan, name: Dahua Lin, name: Ziwei Liu

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/.