Add project video and refresh dataset card

README.md

@@ -8,6 +8,12 @@ task_categories:
 - visual-question-answering
 - image-to-image
 license: other
+tags:
+- spatial-intelligence
+- spatial-reasoning
+- image-generation
+- vision-language-models
+- benchmark
 dataset_info:
 - config_name: default
   features:
@@ -47,75 +53,92 @@ configs:
     path: data/spatialgen_bench.jsonl
 ---
 
+<div align="center">
+
 # SpatialGen-Bench
 
-SpatialGen-Bench is a diagnostic benchmark for spatial cognition in image-generation models and text-output VLMs. The v1.1 release contains **470 samples across 14 subtasks** and four capability levels.
+### Show spatial cognition in generative pixels, not only in text.
 
-| Capability | Subtasks | Samples |
-|---|---|---:|
-| Perception | Counting, Depth, Orientation, Object Size | 145 |
-| Understanding | Relationship, Perspective, Mental Modeling, Spatial Grounding | 140 |
-| Reasoning | Multi-hop, Prediction, Geometric Feasibility | 90 |
-| Interaction | Affordance, Navigation, Trajectory | 95 |
+[![Paper](https://img.shields.io/badge/arXiv-2607.21072-B31B1B?style=flat-square)](https://arxiv.org/abs/2607.21072)
+[![Project](https://img.shields.io/badge/Project-Page-1769AA?style=flat-square)](https://zju-omniai.github.io/ProVisE/)
+[![Code](https://img.shields.io/badge/Code-ProVisE-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/ZJU-OmniAI/ProVisE)
 
-## Files
+</div>
 
-```text
-SpatialGen-Bench/
-├── data/
-│   └── spatialgen_bench.jsonl
-├── benchmarks/
-│   ├── perception/
-│   ├── understanding/
-│   ├── reasoning/
-│   └── interaction/
-├── protocols/
-│   └── spatialgen_bench.yaml
-├── SOURCES.md
-└── LICENSE
-```
+<p align="center">
+  <video controls playsinline preload="metadata" poster="assets/provise-overview-poster.webp" width="90%">
+    <source src="assets/provise-overview.mp4" type="video/mp4">
+  </video>
+</p>
 
-Each JSONL record contains:
+## Benchmark
 
-- `id`: stable sample identifier
-- `capability`: one of the four capability levels
-- `task`: subtask name
-- `image_path`: path relative to `benchmarks/`
-- `media_paths`: all ordered input images for single- or multi-image tasks
-- `mask_path`: optional ground-truth mask path
-- `question`, string-normalized `answer`, `answer_type`, and `choices`
-- `source`, `source_id`, and serialized task-specific `metadata_json`
+<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`](protocols/spatialgen_bench.yaml), with runtime parsers in [ProVisE](https://github.com/ZJU-OmniAI/ProVisE).
 
-Load the benchmark with Hugging Face Datasets:
+## Quick Start
 
 ```python
 from datasets import load_dataset
 
 dataset = load_dataset("wx91726/SpatialGen-Bench", split="test")
+print(dataset[0])
 ```
 
-Media fields contain paths relative to the repository's `benchmarks/` directory. Download the complete evaluation package with:
+Download the complete media and evaluation package for local evaluation:
 
 ```bash
-hf download wx91726/SpatialGen-Bench --repo-type dataset --local-dir SpatialGen-Bench
+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
 ```
 
-The versioned visual-answer contract is provided in [`protocols/spatialgen_bench.yaml`](protocols/spatialgen_bench.yaml). Runtime protocol implementations and parsers are maintained in [ProVisE](https://github.com/XuWang917/ProVisE). See [`SOURCES.md`](SOURCES.md) for task-level provenance, licenses, and upstream media terms.
+- `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
 
-## Acknowledgements
+</details>
 
-We sincerely appreciate [CountBench](https://teaching-clip-to-count.github.io/), [BLINK](https://huggingface.co/datasets/BLINK-Benchmark/BLINK), [EgoOrientBench](https://huggingface.co/datasets/jhCOR/EgoOrientBench), [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning), [ViewSpatial-Bench](https://huggingface.co/datasets/lidingm/ViewSpatial-Bench), [MindCube](https://huggingface.co/datasets/MLL-Lab/MindCube), [VisWorld-Eval](https://github.com/thuml/Reasoning-Visual-World), [RoboAfford-Eval](https://huggingface.co/datasets/tyb197/RoboAfford-Eval), [ShareRobot-Bench](https://huggingface.co/datasets/BAAI/ShareRobot-Bench), [PhysBench](https://huggingface.co/datasets/USC-PSI-Lab/PhysBench), [SPHERE-VLM](https://sphere-vlm.github.io/), and [RefCOCOg](https://github.com/lichengunc/refer) for their public datasets, task designs, and evaluation resources.
+## 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`](SOURCES.md) records task-level provenance, source links, and usage terms.
 
-## License
+<details>
+<summary><strong>Acknowledgements and upstream resources</strong></summary>
+
+We sincerely appreciate [CountBench](https://teaching-clip-to-count.github.io/), [BLINK](https://huggingface.co/datasets/BLINK-Benchmark/BLINK), [EgoOrientBench](https://huggingface.co/datasets/jhCOR/EgoOrientBench), [VSR](https://github.com/cambridgeltl/visual-spatial-reasoning), [ViewSpatial-Bench](https://huggingface.co/datasets/lidingm/ViewSpatial-Bench), [MindCube](https://huggingface.co/datasets/MLL-Lab/MindCube), [VisWorld-Eval](https://github.com/thuml/Reasoning-Visual-World), [RoboAfford-Eval](https://huggingface.co/datasets/tyb197/RoboAfford-Eval), [ShareRobot-Bench](https://huggingface.co/datasets/BAAI/ShareRobot-Bench), [PhysBench](https://huggingface.co/datasets/USC-PSI-Lab/PhysBench), [SPHERE-VLM](https://sphere-vlm.github.io/), and [RefCOCOg](https://github.com/lichengunc/refer) for their public datasets, task designs, and evaluation resources.
 
-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. See [`SOURCES.md`](SOURCES.md) before redistributing or using the media.
+</details>
 
 ## Citation
 
 ```bibtex
-@misc{wang2026showdonttell,
+@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},
-  year   = {2026}
+  journal = {arXiv preprint arXiv:2607.21072},
+  year   = {2026},
+  url    = {https://arxiv.org/abs/2607.21072}
 }
 ```

assets/provise-overview.mp4

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