Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu +4 more
10/23/2023
cs.CVcs.GR

Official code: SUDO-AI-3D/zero123plus (2,096 stars, Apache-2.0, last commit 2024-02-23).

Abstract

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, we develop various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, we showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. The code is available at https://github.com/SUDO-AI-3D/zero123plus.

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Code Implementations(1)

SUDO-AI-3D/zero123plusOfficial100%
Apache-2.0

Code repository for Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model.

2,096149Python, DockerfileOct 16, 20232 years agoApache-2.0
3d3d-graphicsaigcdiffusersdiffusion-models+3 more

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Cite this paper

@article{shi2023zero,
  title  = {Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model},
  author = {Ruoxi Shi and Hansheng Chen and Zhuoyang Zhang and Minghua Liu and Chao Xu and Xinyue Wei and Linghao Chen and Chong Zeng and Hao Su},
  year   = {2023},
  eprint = {2310.15110},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.15110v1}
}

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