Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
Ruoxi Shi1 Hansheng Chen2 Zhuoyang Zhang3 Minghua Liu1 Chao Xu4 Xinyue Wei1 Linghao Chen5 Chong Zeng5 Hao Su1 1UC San Diego 2Stanford University 3Tsinghua University 4UCLA 5Zhejiang University
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 StableDiffusion. 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.
原文 arXiv:2310.15110;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2310.15110v1