README.md

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license: apache-2.0
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 # 🌍 World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning

<p align="center">
  <a href="https://arxiv.org/abs/2604.26934">📄 Paper</a> •
  <a href="https://github.com/WanyueZhang-ai/World2VLM">💻 Code</a> •
  <a href="https://huggingface.co/datasets/WanyueZhang/World2VLM">🤗 Dataset</a>
</p>

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## ✨ Overview

This repository provides a **demo dataset** for the paper:

> **World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning**  
> *Wanyue Zhang et al., 2026*

🔍 **Motivation**  
Vision-Language Models (VLMs) excel at static visual understanding but struggle with **dynamic spatial reasoning**, such as predicting how a scene changes under actions (e.g., moving forward, turning). 

💡 **Key Idea**  
We introduce **World2VLM**, a framework that uses **world models as training-time teachers** to distill *spatial imagination* into VLMs—enabling them to reason about **future views and action consequences without external simulation at inference time**. 

📦 **This repository** contains:
- A **compact demo dataset** showcasing the data construction pipeline
- Representative **trajectory-based supervision samples**
- Examples of **8 dynamic spatial reasoning task types**

⚠️ The **full dataset will be released soon**.

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## 🧠 What is World2VLM?

World2VLM trains VLMs to **mentally simulate the world** by learning from world-model-generated transitions:

- Input: an image + an action (e.g., move forward)
- World model: generates the future view
- Output: structured supervision for reasoning

This enables two key capabilities:
- 🔁 **Inverse reasoning**: infer the action from image changes  
- 🔮 **Forward reasoning**: predict what happens after an action  

Unlike prior work, **no world model is needed at inference time**.

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## 📂 Dataset Structure

```bash
data-demo/
├── README.md
├── SVC-RealScene-demo
│   ├── tasks_demo.jsonl
│   └── scenes/demo_scene/...
├── SVC-SimulatedScene-demo
│   ├── tasks_demo.jsonl
│   └── scenes/demo_scene/...
├── HY-WorldPlay-RealScene-demo
│   ├── tasks_demo.jsonl
│   └── scenes/demo_scene/...
└── HY-WorldPlay-SimulatedScene-demo
    ├── tasks_demo.jsonl
    └── scenes/demo_scene/...
```

## 🔍 Included Demo Subsets

We provide **four compact subsets** covering:

| Teacher Model | Scene Type | Description |
|--------------|------------|-------------|
| SVC | Real Scene | Camera-conditioned view synthesis |
| SVC | Simulated Scene | Synthetic environment transitions |
| HY-WorldPlay | Real Scene | Action-conditioned world dynamics |
| HY-WorldPlay | Simulated Scene | Long-horizon simulated trajectories |

Each subset includes:
- 🎬 A **trajectory bundle** (images + metadata)
- 📝 A **`tasks_demo.jsonl`** file with structured supervision

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## 🧾 Data Format

Each line in `tasks_demo.jsonl` represents one training example.

### Common Fields

- `task_type`  
  One of 8 spatial reasoning tasks: `A1–A4`, `D1–D4`

- `messages`  
  A two-turn conversation:
  - User prompt  
  - Target answer  

- `images`  
  Relative paths to referenced images

---

## 🧩 Task Suite (8 Types)

World2VLM defines a **bidirectional task suite**:

### 🔁 Motion-Centric (A-series)

| Task | Description |
|------|-------------|
| A1 | Motion distance estimation |
| A2 | Motion orientation estimation |
| A3 | Multi-step motion prediction |
| A4 | Action-sequence verification |

### 🎯 Object-Centric (D-series)

| Task | Description |
|------|-------------|
| D1 | Post-action bounding box prediction |
| D2 | Post-action visibility detection |
| D3 | Cross-view action inference |
| D4 | Object consistency across views |

💡 These tasks jointly enforce:
- Understanding **camera motion**
- Tracking **object transformations**
- Reasoning about **viewpoint changes**

---

## ⚙️ Data Construction Pipeline

The dataset is generated using **world models as teachers**:

1. 🖼️ Start from an **anchor image**  
2. 🎮 Sample an **egocentric action sequence**  
3. 🌐 Generate **future views** via world models  
4. 🧠 Convert transitions into:
   - Forward tasks (predict outcomes)  
   - Inverse tasks (recover actions)  

This yields structured supervision of the form:

- `P(action | before, after)` (inverse)  
- `P(outcome | before, action)` (forward)  

---

## 🚀 Key Features

- 🧠 **Spatial imagination distilled into VLMs**
- 🔄 **Bidirectional reasoning supervision**
- 🧩 **Multi-task structured dataset**
- ⚡ **No world model needed at inference**
- 🌍 Supports both **real and simulated scenes**

---

## 📊 Why This Matters

World2VLM addresses a core limitation:

> ❌ VLMs fail at mental simulation  
> ✅ World models can simulate—but are expensive  

👉 **Our solution:**  
Train VLMs to *internalize* world-model reasoning.

This leads to:
- Better **dynamic spatial reasoning**
- Lower **inference cost**
- Improved performance on spatial reasoning benchmarks

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## 📝 Notes

- This repo contains **demo-scale data only**
- Full dataset (~100K samples) will be released soon
- Demo is intended for:
  - 🔍 Format inspection  
  - 🧪 Pipeline understanding  
  - 🧠 Task design exploration  

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## 📚 Citation

If you find this work useful, please cite:

```
@misc{zhang2026world2vlmdistillingworldmodel,
      title={World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning}, 
      author={Wanyue Zhang and Wenxiang Wu and Wang Xu and Jiaxin Luo and Helu Zhi and Yibin Huang and Shuo Ren and Zitao Liu and Jiajun Zhang},
      year={2026},
      eprint={2604.26934},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.26934}, 
}
```

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## 🤝 Acknowledgements

We thank the community for advances in:

* 🌐 World Models (e.g., SVC(https://arxiv.org/abs/2503.14489), HY-WorldPlay(https://arxiv.org/abs/2412.03603))
* 🤖 Vision-Language Models
* 🧠 Spatial reasoning benchmarks

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## 📬 Contact

For questions or collaborations, please open an issue or contact the authors via the paper.

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