Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
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/.
6895575348b0ae5ca2710d11
Yuhan Zhang
claimed_verified
2025-08-08T16:20:09.208Z
604599a0e6aa3e130cb9286c
/avatars/c9c723a4911b1a1b8870f44595fc9ca6.svg
Zyh
user
ZhangYuhan
6895575348b0ae5ca2710d12
Long Zhuo
6895575348b0ae5ca2710d13
Ziyang Chu
6895575348b0ae5ca2710d14
Tong Wu
6895575348b0ae5ca2710d15
Zhibing Li
6895575348b0ae5ca2710d16
Liang Pan
6895575348b0ae5ca2710d17
Dahua Lin
6895575348b0ae5ca2710d18
Ziwei Liu
Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
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/.
Hi3DEval is a hierarchical evaluation framework for 3D generative content that combines object-level and part-level assessments, including material realism, using a large-scale dataset and hybrid 3D representations.
Hi3DEval hierarchical evaluation framework object-level evaluation part-level evaluation material realism Hi3DBench multi-agent annotation pipeline 3D-aware automated scoring system video-based representations pretrained 3D features spatio-temporal consistency part-level perception