Instructions to use JaceyH919/Gen3R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JaceyH919/Gen3R with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JaceyH919/Gen3R", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
library_name: diffusers
pipeline_tag: image-to-3d
tags:
- 3D scene generation
base_model:
- alibaba-pai/Wan2.1-Fun-V1.1-1.3B-Control-Camera
Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction
Jiaxin Huang, Yuanbo Yang, Bangbang Yang, Lin Ma, Yuewen Ma, Yiyi Liao
This model represents the official checkpoint of the paper titled "Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction".
๐ Citation
Please cite our paper if you find this repository useful:
@misc{huang2026gen3r3dscenegeneration,
title={Gen3R: 3D Scene Generation Meets Feed-Forward Reconstruction},
author={Jiaxin Huang and Yuanbo Yang and Bangbang Yang and Lin Ma and Yuewen Ma and Yiyi Liao},
year={2026},
eprint={2601.04090},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.04090},
}