Recent advances in text-to-image diffusion models have been driven by the increasing availability of paired 2D data. However, the development of 3D diffusion models has been hindered by the scarcity of high-quality 3D data, resulting in less competitive performance compared to their 2D counterparts. To address this challenge, we propose repurposing pre-trained 2D diffusion models for 3D object generation. We introduce Gaussian Atlas, a novel representation that utilizes dense 2D grids, enabling the fine-tuning of 2D diffusion models to generate 3D Gaussians. Our approach demonstrates successful transfer learning from a pre-trained 2D diffusion model to a 2D manifold flattened from 3D structures. To support model training, we compile GaussianVerse, a large-scale dataset comprising 205K high-quality 3D Gaussian fittings of various 3D objects. Our experimental results show that text-to-image diffusion models can be effectively adapted for 3D content generation, bridging the gap between 2D and 3D modeling.
@article{arxiv.2503.15877,
title = {Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation},
author = {Tiange Xiang and Kai Li and Chengjiang Long and Christian Häne and Peihong Guo and Scott Delp and Ehsan Adeli and Li Fei-Fei},
journal= {arXiv preprint arXiv:2503.15877},
year = {2025}
}