提出多维评估框架与新指标，系统评测文生3D生成质量
Benchmarking and Learning Multi-Dimensional Quality Evaluator for Text-to-3D Generation
- 构建覆盖8类提示的1280个纹理网格基准集
- 在107,520条主观标注上验证多维评估有效性
- 用超网络生成维度专用映射函数，支持多维评分
近年来文本生成3D取得显著进展，但评估仍面临双重挑战：现有基准缺乏对不同提示类别和评估维度的细粒度划分；以往指标仅关注单一维度（如文本-3D对齐），难以实现多维质量评估。为此，我们首先提出综合性基准MATE-3D，包含8个精心设计的提示类别，涵盖单/多物体生成，共生成1,280个带纹理的网格模型。我们开展了大规模主观实验，从四个不同维度收集107,520条标注，并进行详细分析。基于MATE-3D，我们提出新型质量评估器HyperScore，利用超网络为每个评估维度生成特定映射函数，可有效实现多维质量评估。HyperScore在MATE-3D上优于现有指标，展现出评估与改进文生3D生成的潜力。项目地址：https://mate-3d.github.io/
原文摘要 · Abstract (English)
Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., text-3D alignment) and fail to perform multi-dimensional quality assessment. To address these problems, we first propose a comprehensive benchmark named MATE-3D. The benchmark contains eight well-designed prompt categories that cover single and multiple object generation, resulting in 1,280 generated textured meshes. We have conducted a large-scale subjective experiment from four different evaluation dimensions and collected 107,520 annotations, followed by detailed analyses of the results. Based on MATE-3D, we propose a novel quality evaluator named HyperScore. Utilizing hypernetwork to generate specified mapping functions for each evaluation dimension, our metric can effectively perform multi-dimensional quality assessment. HyperScore presents superior performance over existing metrics on MATE-3D, making it a promising metric for assessing and improving text-to-3D generation. The project is available at https://mate-3d.github.io/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。