---
license: apache-2.0
base_model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
pipeline_tag: image-to-video
tags:
  - world-model
  - camera-control
  - video-generation
  - long-video
---

<div align="center">

<h2>Memorizon: Training World Models Beyond Their Context Window</h2>

<p>
  <a href="https://tingtingliao.github.io/" style="color:#0969da"><b>Tingting Liao</b></a> &nbsp;·&nbsp;
  <span style="color:#0969da"><b>Xuezhi Liang</b></span> &nbsp;·&nbsp;
  <a href="https://www.hao-li.com/Hao_Li/Hao_Li_-_about_me.html" style="color:#0969da"><b>Hao Li</b></a> &nbsp;·&nbsp;
  <a href="https://guangyiliu.com/" style="color:#0969da"><b>Guangyi Liu</b></a>
  <br>
  <sub>Institute of Foundation Models (IFM), MBZUAI</sub>
</p>

<div align="center">
  <a href='https://github.com/TingtingLiao/memorizon'><img src='https://img.shields.io/badge/GitHub-code-black?logo=github&logoColor=black&labelColor=white'></a>  &ensp;
  <a href='https://tingtingliao.github.io/memorizon/'><img src='https://img.shields.io/badge/project-page-green?logo=googlechrome&logoColor=green&labelColor=white'></a>  &ensp;
  <a href='https://arxiv.org/abs/2610.00544'><img src='https://img.shields.io/badge/arXiv-2610.00544-red?logo=arxiv&logoColor=darkred&labelColor=white'></a>  &ensp;
</div>

<br>

<img src="teaser.webp" width="100%">

</div>

---

## ✨ This checkpoint

| | |
|---|---|
| **Model** | Memorizon, 4-step distilled (Self-Forcing + DMD, CFG distilled in) |
| **Base** | Wan2.2-TI2V-5B |
| **Input** | one image + keyboard actions or a camera trajectory |
| **Output** | 864×480, 16 fps, generated one second at a time |
| **Sampling** | 4 steps, no CFG (read from the config) |

## 🚀 Usage

```bash
git clone https://github.com/TingtingLiao/memorizon.git && cd memorizon && pip install -e .

python scripts/generate.py --checkpoint Luffuly/memorizon --image photo.jpg \
    --actions "w*16 l*24 w*16 l*24" --output out.mp4
```

```python
from memorizon import MemorizonPipeline, actions_to_c2w, save_video

pipe = MemorizonPipeline.from_pretrained("Luffuly/memorizon")
video = pipe("photo.jpg", actions_to_c2w("w*16 l*24 w*16 l*24"))
save_video(video, "out.mp4")
```

### 🎮 Keyboard actions — one per 0.25 s

| Key | Action | Key | Action |
|:---:|:---|:---:|:---|
| `w` `s` | forward / backward · 0.25 m | `j` `l` | turn left / right · 7.5° |
| `a` `d` | left / right · 0.25 m | `i` `k` | look up / down · 7.5° |
| `.` | stay | `*N` | repeat N times |

<details>
<summary><b>Prompts</b> — captioned automatically</summary>

<br>

Without a prompt, the image is captioned by Qwen3-VL-8B in the training format
(`memorizon/caption.py`). Custom prompts should follow it — a perspective prefix,
then one paragraph:

> First-person perspective — character not visible. A winding paved path curves
> beneath a canopy of vibrant pink cherry blossoms, …

</details>

<details>
<summary><b>Camera trajectories</b></summary>

<br>

Instead of actions, pass `--trajectory` a `[T, 4, 4]` array of camera-to-world poses,
`T = 1 + 4 × seconds`, in the first frame's coordinates (OpenCV axes), translations
in metres / 4.

</details>

## 📖 Citation

```bibtex
@article{memorizon2026,
  title   = {Memorizon: Training World Models Beyond Their Context Window},
  author  = {Tingting Liao, Xuezhi Liang, Hao Li, Guangyi Liu},
  journal = {arXiv preprint arXiv:2610.00544},
  year    = {2026}
}
```