One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View
  Generation and 3D Diffusion
Minghua Liu Ruoxi Shi Linghao Chen Zhuoyang Zhang Chao Xu Xinyue Wei Hansheng Chen Chong Zeng Jiayuan Gu Hao Su
Recent advancements in open-world 3D object generation have been remarkable,
with image-to-3D methods offering superior fine-grained control over their
text-to-3D counterparts. However, most existing models fall short in
simultaneously providing rapid generation speeds and high fidelity to input
images - two features essential for practical applications. In this paper, we
present One-2-3-45++, an innovative method that transforms a single image into
a detailed 3D textured mesh in approximately one minute. Our approach aims to
fully harness the extensive knowledge embedded in 2D diffusion models and
priors from valuable yet limited 3D data. This is achieved by initially
finetuning a 2D diffusion model for consistent multi-view image generation,
followed by elevating these images to 3D with the aid of multi-view conditioned
3D native diffusion models. Extensive experimental evaluations demonstrate that
our method can produce high-quality, diverse 3D assets that closely mirror the
original input image. Our project webpage:
https://sudo-ai-3d.github.io/One2345plus_page.