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--- |
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license: apache-2.0 |
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task_categories: |
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- text-to-3d |
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--- |
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# LEGO-Bench |
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**LEGO-Bench** is a benchmark designed to evaluate text-guided 3D scene synthesis using fine-grained, realistic instructions. |
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Each instruction contains multiple constraints describing layout, materials, objects, and placements, reflecting the compositional complexity of real-world indoor scenes. |
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The dataset includes 130 instructions paired with manually aligned 3D scenes, totaling 1,250 annotated constraints. |
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On average, each instruction contains about 10 constraints, covering both architectural elements (walls, floors, doors, windows) and object-level relationships. |
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Together, these detailed annotations enable systematic, constraint-level evaluation of how well generated scenes satisfy natural-language specifications. |
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## Schema |
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**full_data (json)**: contains instructions and the constraints within the instruction. |
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**data_0 ~ data_129 (folder)**: contains the scene that aligns with the instruction in json format. |
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---- |
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# Evaluating LEGO-Bench with LEGO-Eval |
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**LEGO-Eval** is a tool-augmented evaluation framework for text-guided 3D scene synthesis. |
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It enables fine-grained and interpretable assessment of instruction-scene alignment by |
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grounding scene components using a diverse suite of 21 multimodal tools, supporting |
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multi-hop reasoning over spatial and attribute constraints. |
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---- |
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# Citation |
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If you use this dataset, please cite: |
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```bibtex |
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@article{hwangbo2025lego, |
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title={LEGO-Eval: Towards Fine-Grained Evaluation on Synthesizing 3D Embodied Environments with Tool Augmentation}, |
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author={Hwangbo, Gyeom and Chae, Hyungjoo and Kang, Minseok and Ju, Hyeonjong and Oh, Soohyun and Yeo, Jinyoung}, |
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journal={arXiv preprint arXiv:2511.03001}, |
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year={2025}}} |
