  In this work, we investigate the problem of creating high-fidelity 3D content
from only a single image. This is inherently challenging: it essentially
involves estimating the underlying 3D geometry while simultaneously
hallucinating unseen textures. To address this challenge, we leverage prior
knowledge from a well-trained 2D diffusion model to act as 3D-aware supervision
for 3D creation. Our approach, Make-It-3D, employs a two-stage optimization
pipeline: the first stage optimizes a neural radiance field by incorporating
constraints from the reference image at the frontal view and diffusion prior at
novel views; the second stage transforms the coarse model into textured point
clouds and further elevates the realism with diffusion prior while leveraging
the high-quality textures from the reference image. Extensive experiments
demonstrate that our method outperforms prior works by a large margin,
resulting in faithful reconstructions and impressive visual quality. Our method
presents the first attempt to achieve high-quality 3D creation from a single
image for general objects and enables various applications such as text-to-3D
creation and texture editing.
