metadata

license: cc-by-nc-2.0
pipeline_tag: image-to-3d
library_name: transformers
datasets:
  - argilla/FinePersonas-v0.1
language:
  - am
metrics:
  - accuracy
base_model:
  - stepfun-ai/GOT-OCR2_0
new_version: meta-llama/Llama-3.2-11B-Vision-Instruct
tags:
  - chemistry

[ECCV 2024] VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models

Porject page, Paper link

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
Junlin Han, Filippos Kokkinos, Philip Torr
GenAI, Meta and TVG, University of Oxford
European Conference on Computer Vision (ECCV), 2024

News

[08.08.2024] HF Demo is available, big thanks to Jade Choghari's help for making it possible.
[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:

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

Load model directly

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 to see VFusion3D in action! 🤗

Results and Comparisons

3D Generation Results

User Study Results

Acknowledgement

This inference code of VFusion3D heavily borrows from 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.