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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+ # 🌍 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>
+
+---
+
+## ✨ 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).  [oai_citation:0‡2604.26934v1.pdf](sediment://file_000000007620720b9a6b178b8a8dc3c9)  
+
+💡 **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**.  [oai_citation:1‡2604.26934v1.pdf](sediment://file_000000007620720b9a6b178b8a8dc3c9)  
+
+📦 **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**.
+
+---
+
+## 🧠 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**.
+
+---
+
+## 📂 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
+
+---
+
+## 🧾 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
+
+---
+
+## 📝 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  
+
+---
+## 📚 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}, 
+}
+```
+
+---
+## 🤝 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
+
+---
+## 📬 Contact
+
+For questions or collaborations, please open an issue or contact the authors via the paper.
+
+---