Datasets:
license: cc-by-4.0
task_categories:
- text-to-3d
language:
- en
tags:
- benchmark
- evaluation
- multi-dimensional
- text-to-3d
- 3d-generation
pretty_name: T23D-CompBench
size_categories:
- 1K<n<10K
Text-to-3D Comprehensive Benchmark (T23D-CompBench) 🎥📊
Code · Project Page · Paper@ArXiv · Prompt list
Welcome to the T23D-CompBench dataset! This repository contains around 3,600 textured meshes generated by various models using the Prompt list. These textured meshes have been annotated from twelve evaluation dimensions, including Object Alignment, Attribute Alignment, Interaction Alignment, Overall Alignment, Texture Clarity, Texture Aesthetics, Geometry Loss, Geometry Redundancy, Geometry Roughness, Overall Visual Quality, 3D Authentic, and Overall Quality.
Dataset Details 📚
Paper: Read the Paper
Code: Code
Prompt List (360 prompts): Prompt list
Project Page: Project Page
Models Included in T23D-CompBench dataset:
Dataset Structure: Generate textured meshes are organized in the following structure (take 3DTopia for an example)
./3DTopia.zip/ ├── A_asymmetrical_flower ├── model_normalized.obj ├── model_normalized.obj.mtl ├── material_0.png ├── A_bird_on_a_branch ├── model_normalized.obj ├── model_normalized.obj.mtl ├── material_0.png ...
Acknowledgements and Citation 🙏
This dataset is based on the text-to-3D generative framework, which utilizes various open-source repositories for textured mesh generation evaluation. If you find this dataset helpful, please consider citing the original work:
@article{cui2025towards,
title={Towards Fine-Grained Text-to-3D Quality Assessment: A Database and A Two-Stage Rank-Learning Metric},
author={Bingyang Cui, Yujie Zhang, Qi Yang, Zhu Li, and Yiling Xu},
journal={arXiv preprint arXiv:2509.23841},
year={2025}
}