  Reconstructing 3D objects from a single image guided by pretrained diffusion
models has demonstrated promising outcomes. However, due to utilizing the
case-agnostic rigid strategy, their generalization ability to arbitrary cases
and the 3D consistency of reconstruction are still poor. In this work, we
propose Consistent123, a case-aware two-stage method for highly consistent 3D
asset reconstruction from one image with both 2D and 3D diffusion priors. In
the first stage, Consistent123 utilizes only 3D structural priors for
sufficient geometry exploitation, with a CLIP-based case-aware adaptive
detection mechanism embedded within this process. In the second stage, 2D
texture priors are introduced and progressively take on a dominant guiding
role, delicately sculpting the details of the 3D model. Consistent123 aligns
more closely with the evolving trends in guidance requirements, adaptively
providing adequate 3D geometric initialization and suitable 2D texture
refinement for different objects. Consistent123 can obtain highly 3D-consistent
reconstruction and exhibits strong generalization ability across various
objects. Qualitative and quantitative experiments show that our method
significantly outperforms state-of-the-art image-to-3D methods. See
https://Consistent123.github.io for a more comprehensive exploration of our
generated 3D assets.
