---
license: cc-by-nc-2.0
pipeline_tag: image-to-3d
---
# [ECCV 2024] VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models

[Porject page](https://junlinhan.github.io/projects/vfusion3d.html), [Paper link](https://arxiv.org/abs/2403.12034)

VFusion3D is a large, feed-forward 3D generative model trained with a small amount of 3D data and a large volume of synthetic multi-view data. It is the first work exploring scalable 3D generative/reconstruction models as a step towards a 3D foundation.

[VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models](https://junlinhan.github.io/projects/vfusion3d.html)<br>
[Junlin Han](https://junlinhan.github.io/), [Filippos Kokkinos](https://www.fkokkinos.com/), [Philip Torr](https://www.robots.ox.ac.uk/~phst/)<br>
GenAI, Meta and TVG, University of Oxford<br>
European Conference on Computer Vision (ECCV), 2024


## News

- [25.07.2024] Release weights and inference code for VFusion3D.



## Quick Start

Getting started with VFusion3D is super easy! 🤗 Here’s how you can use the model with Hugging Face:

### Install Dependencies (Optional)

Depending on your needs, you may want to enable specific features like mesh generation or video rendering. We've got you covered with these additional packages:

```bash
!pip --quiet install imageio[ffmpeg] PyMCubes trimesh rembg[gpu,cli] kiui
```

### Load model directly
```python
import torch
from transformers import AutoModel, AutoProcessor

# load the model and processor
model = AutoModel.from_pretrained("jadechoghari/vfusion3d", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("jadechoghari/vfusion3d")

# download and preprocess the image
import requests
from PIL import Image
from io import BytesIO

image_url = 'https://sm.ign.com/ign_nordic/cover/a/avatar-gen/avatar-generations_prsz.jpg'
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))

# preprocess the image and get the source camera 
image, source_camera = processor(image)


# generate planes (default output)
output_planes = model(image, source_camera)
print("Planes shape:", output_planes.shape)

# generate a 3D mesh
output_planes, mesh_path = model(image, source_camera, export_mesh=True)
print("Planes shape:", output_planes.shape)
print("Mesh saved at:", mesh_path)

# Generate a video
output_planes, video_path = model(image, source_camera, export_video=True)
print("Planes shape:", output_planes.shape)
print("Video saved at:", video_path)

```
- **Default (Planes):** By default, VFusion3D outputs planes—ideal for further 3D operations.
- **Export Mesh:** Want a 3D mesh? Just set `export_mesh=True`, and you'll get a `.obj` file ready to roll. You can also customize the mesh resolution by adjusting the `mesh_size` parameter.
- **Export Video:** Fancy a 3D video? Set `export_video=True`, and you'll receive a beautifully rendered video from multiple angles. You can tweak `render_size` and `fps` to get the video just right.

Check out our [demo app](https://huggingface.co/spaces/jadechoghari/vfusion3d-app) to see VFusion3D in action! 🤗

## Results and Comparisons

### 3D Generation Results
<img src='assets/gif1.gif' width=950>

<img src='assets/gif2.gif' width=950>

### User Study Results
<img src='assets/user.png' width=950>



## Acknowledgement

- This inference code of VFusion3D heavily borrows from [OpenLRM](https://github.com/3DTopia/OpenLRM).  

## Citation

If you find this work useful, please cite us:


```
@article{han2024vfusion3d,
  title={VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models},
  author={Junlin Han and Filippos Kokkinos and Philip Torr},
  journal={European Conference on Computer Vision (ECCV)},
  year={2024}
}
```

## License

- The majority of VFusion3D is licensed under CC-BY-NC, however portions of the project are available under separate license terms: OpenLRM as a whole is licensed under the Apache License, Version 2.0, while certain components are covered by NVIDIA's proprietary license.
- The model weights of VFusion3D is also licensed under CC-BY-NC.