Hi3DEval: Advancing 3DGeneration Evaluation with Hierarchical Validity
–Neural Information Processing Systems
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.
Neural Information Processing Systems
Jun-16-2026, 20:14:46 GMT
- Country:
- Asia (0.28)
- Genre:
- Research Report
- New Finding (1.00)
- Experimental Study (1.00)
- Research Report
- Technology:
- Information Technology > Artificial Intelligence
- Vision (1.00)
- Representation & Reasoning (1.00)
- Natural Language > Large Language Model (1.00)
- Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence