GEdit3D-Bench / README.md
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metadata
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

Alchemy3D GEdit3D-Bench

A large-scale, open-world benchmark for versatile 3D asset editing.

Project Page arXiv PDF Code Evaluation Hugging Face Model Dataset ModelScope Dataset

GEdit3D-Bench construction and multi-dimensional evaluation overview.

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}, 
}