Download README.md from libadi/GEdit3D-Bench: direct link, hf CLI and curl.
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https://huggingface.co/datasets/libadi/GEdit3D-Bench/resolve/main/README.md
- Command line
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hf download hf://datasets/libadi/GEdit3D-Bench/README.md
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curl -L -o README.md https://huggingface.co/datasets/libadi/GEdit3D-Bench/resolve/main/README.md
license: apache-2.0
task_categories:
- image-to-3d
- text-to-3d
language:
- en
size_categories:
- 1K<n<10K
tags:
- 3d
- 3d-editing
- benchmark
- evaluation
GEdit3D-Bench
A large-scale, open-world benchmark for versatile 3D asset editing.
GEdit3D-Bench is the evaluation benchmark for Alchemy3D: an open-world 3D editing suite built from assets independent of the training data (newly synthesized content and curated Sketchfab assets). It contains 2,100 editing pairs over 6 edit types. Each item provides a source asset, source / target captions, source / target images, and an editing instruction. Evaluation jointly measures view quality, reference alignment, and MLLM-based scores.
A ModelScope mirror is also available at libd55/GEdit3D-Bench.
Dataset Layout
The benchmark contains source assets (glbs/), condition images (images/), and metadata CSVs. metadata.csv indexes the full set (2,100 pairs across add, remove, replace, local_appearance, global_appearance, animation). metadata_small.csv is the subset used for MLLM / human scoring (420 pairs), kept smaller because these evaluations are expensive.
Each row provides a source asset, source/target captions, source/target images, an editing instruction, and an edit type.
Usage
Download the benchmark:
hf download libadi/GEdit3D-Bench --repo-type dataset --local-dir GEdit3D-Bench
Evaluation code and baselines would be released at edit3dstudio.
License
Apache License 2.0. Downstream 3D generators and renderers you apply to these assets may have their own terms. Sketchfab-sourced assets may carry additional terms from their original licenses.
Citation
@misc{li2026scalingversatile3dassets,
title={Scaling Versatile 3D Assets Editing with a Million-Scale Dataset},
author={Badi Li and Tianxin Huang and Yu Zhou and Wei-Shi Zheng and Yi Ma and Shenghua Gao},
year={2026},
eprint={2609.34271},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2609.34271},
}