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wx91726/SpatialGen-Bench

Benchmark Each record retains its source-task metric target as integer, text, point, mask, or polyline. The frozen visual-answer contract is available at protocols/spatialgen_bench.yaml, with runtime parsers in ProVisE. Quick Start from datasets import load_dataset dataset = load_dataset("wx91726/SpatialGen-Bench", split="test") print(dataset[0]) Download the complete media and evaluation package for local evaluation: hf download… See the full description on the dataset page: https://huggingface.co/datasets/wx91726/SpatialGen-Bench.

sourceHugging Faceotherupdated 2d agoView on Hugging Face
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Dataset Card

<p align="center"> <video controls playsinline preload="metadata" poster="https://huggingface.co/datasets/wx91726/SpatialGen-Bench/resolve/main/assets/provise-overview-poster.webp" width="90%"> <source src="https://huggingface.co/datasets/wx91726/SpatialGen-Bench/resolve/main/assets/provise-overview.mp4" type="video/mp4"> </video> </p>

<p align="center"> <a href="https://arxiv.org/abs/2607.21072"><img src="https://img.shields.io/badge/arXiv-2607.21072-B31B1B?style=flat-square" alt="Paper"></a> <a href="https://zju-omniai.github.io/ProVisE/"><img src="https://img.shields.io/badge/Project-Page-1769AA?style=flat-square" alt="Project"></a> <a href="https://github.com/ZJU-OmniAI/ProVisE"><img src="https://img.shields.io/badge/Code-ProVisE-181717?style=flat-square&amp;logo=github&amp;logoColor=white" alt="Code"></a> </p>

Benchmark

<p align="center"> <img src="assets/benchmark_overview.webp" alt="SpatialGen-Bench task overview across perception, understanding, reasoning, and interaction." width="100%"> </p>

Each record retains its source-task metric target as integer, text, point, mask, or polyline. The frozen visual-answer contract is available at `protocols/spatialgen_bench.yaml`, with runtime parsers in ProVisE.

Quick Start

python
from datasets import load_dataset

dataset = load_dataset("wx91726/SpatialGen-Bench", split="test")
print(dataset[0])

Download the complete media and evaluation package for local evaluation:

bash
hf download wx91726/SpatialGen-Bench \
  --repo-type dataset \
  --local-dir SpatialGen-Bench

<details> <summary><strong>Dataset structure and JSONL schema</strong></summary>

text
SpatialGen-Bench/
|-- data/spatialgen_bench.jsonl
|-- benchmarks/{perception,understanding,reasoning,interaction}/
|-- protocols/spatialgen_bench.yaml
|-- SOURCES.md
`-- LICENSE
  • —id: stable sample identifier
  • —capability: one of the four capability levels
  • —task: subtask name
  • —image_path: primary input path relative to benchmarks/
  • —media_paths: ordered inputs for single- or multi-image tasks
  • —mask_path: optional ground-truth mask path
  • —question, normalized answer, answer_type, and choices
  • —source and source_id: upstream provenance
  • —metadata_json: serialized task-specific evaluator metadata

</details>

Sources and License

SpatialGen-Bench annotations and protocol configuration are released under Apache-2.0. Source-derived media remains subject to the licenses and terms of its upstream datasets. `SOURCES.md` records task-level provenance, source links, and usage terms.

<details> <summary><strong>Acknowledgements and upstream resources</strong></summary>

We sincerely appreciate CountBench, BLINK, EgoOrientBench, VSR, ViewSpatial-Bench, MindCube, VisWorld-Eval, RoboAfford-Eval, ShareRobot-Bench, PhysBench, SPHERE-VLM, and RefCOCOg for their public datasets, task designs, and evaluation resources.

</details>

Citation

bibtex
@article{wang2026showdonttell,
  title  = {Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text},
  author = {Wang, Xu and Yao, Kaixiang and Pan, Miao and Zhou, Xiaohe and Liu, Xuanyu and Zhang, Wenqi and Zhang, Xuhong},
  journal = {arXiv preprint arXiv:2607.21072},
  year   = {2026},
  url    = {https://arxiv.org/abs/2607.21072}
}