Datasets:
Tasks:
Visual Question Answering
Modalities:
Image
Languages:
English
Size:
1K<n<10K
ArXiv:
License:
| license: mit | |
| task_categories: | |
| - visual-question-answering | |
| language: | |
| - en | |
| tags: | |
| - visual reason | |
| - transformation | |
| - benchmark | |
| - computer vision | |
| size_categories: | |
| - 1K<n<10K | |
| # VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning | |
| [](http://arxiv.org/abs/2508.04043) | |
| ## Dataset Description | |
| VisualTrans is the first comprehensive benchmark specifically designed for Visual Transformation Reasoning (VTR) in real-world human-object interaction scenarios. The benchmark encompasses 12 semantically diverse manipulation tasks and systematically evaluates three essential reasoning dimensions through 6 well-defined subtask types. | |
| ## Dataset Statistics | |
| - **Total samples**: 497 | |
| - **Number of manipulation scenarios**: 12 | |
| - **Task types**: 6 | |
| ### Task Type Distribution | |
| - **count**: 63 samples (12.7%) | |
| - **procedural_causal**: 86 samples (17.3%) | |
| - **procedural_interm**: 88 samples (17.7%) | |
| - **procedural_plan**: 42 samples (8.5%) | |
| - **spatial_fine_grained**: 168 samples (33.8%) | |
| - **spatial_global**: 50 samples (10.1%) | |
| ### Manipulation Scenarios | |
| The benchmark covers 12 diverse manipulation scenarios: | |
| - Add Remove Lid | |
| - Assemble Disassemble Legos | |
| - Build Unstack Lego | |
| - Insert Remove Bookshelf | |
| - Insert Remove Cups From Rack | |
| - Make Sandwich | |
| - Pick Place Food | |
| - Play Reset Connect Four | |
| - Screw Unscrew Fingers Fixture | |
| - Setup Cleanup Table | |
| - Sort Beads | |
| - Stack Unstack Bowls | |
| ## Dataset Structure | |
| ### Files | |
| - `VisualTrans.json`: Main benchmark file containing questions, answers, and image paths | |
| - `images.zip`: Compressed archive containing all images used in the benchmark | |
| ### Data Format | |
| Each sample in the benchmark contains: | |
| ```json | |
| { | |
| "task_type": "what", | |
| "images": [ | |
| "scene_name/image1.jpg", | |
| "scene_name/image2.jpg" | |
| ], | |
| "scene": "scene_name", | |
| "question": "Question about the transformation", | |
| "label": "Ground truth answer" | |
| } | |
| ``` | |
| ## Reasoning Dimensions | |
| The framework evaluates three essential reasoning dimensions: | |
| 1. **Quantitative Reasoning** - Counting and numerical reasoning tasks | |
| 2. **Procedural Reasoning** | |
| - **Intermediate State** - Understanding process states during transformation | |
| - **Causal Reasoning** - Analyzing cause-effect relationships | |
| - **Transformation Planning** - Multi-step planning and sequence reasoning | |
| 3. **Spatial Reasoning** | |
| - **Fine-grained** - Precise spatial relationships and object positioning | |
| - **Global** - Overall spatial configuration and scene understanding | |
| ## Usage | |
| ```python | |
| import json | |
| import zipfile | |
| # Load the benchmark data | |
| with open('VisualTrans.json', 'r') as f: | |
| benchmark_data = json.load(f) | |
| # Extract images | |
| with zipfile.ZipFile('images.zip', 'r') as zip_ref: | |
| zip_ref.extractall('images/') | |
| # Access a sample | |
| sample = benchmark_data[0] | |
| print(f"Question: {sample['question']}") | |
| print(f"Answer: {sample['label']}") | |
| print(f"Images: {sample['images']}") | |
| ``` | |
| ## Citation | |
| If you use this benchmark, please cite our work: | |
| ```bibtex | |
| @misc{ji2025visualtransbenchmarkrealworldvisual, | |
| title={VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning}, | |
| author={Yuheng Ji and Yipu Wang and Yuyang Liu and Xiaoshuai Hao and Yue Liu and Yuting Zhao and Huaihai Lyu and Xiaolong Zheng}, | |
| year={2025}, | |
| eprint={2508.04043}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2508.04043}, | |
| } | |
| ``` | |
| ## License | |
| This dataset is released under the MIT License. | |
| ## Contact | |
| For questions or issues, please open an issue on our [GitHub repository](https://github.com/WangYipu2002/VisualTrans) or contact the authors. |