README.md

metadata

license: mit
task_categories:
  - visual-question-answering
  - image-classification
language:
  - en
tags:
  - visual reason
  - transformation
  - benchmark
  - computer vision
size_categories:
  - 1K<n<10K

VisualTrans: A Benchmark for Real-World Visual Transformation Reasoning

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:

{
    "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

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:

@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 or contact the authors.