  We present Magic123, a two-stage coarse-to-fine approach for high-quality,
textured 3D meshes generation from a single unposed image in the wild using
both2D and 3D priors. In the first stage, we optimize a neural radiance field
to produce a coarse geometry. In the second stage, we adopt a memory-efficient
differentiable mesh representation to yield a high-resolution mesh with a
visually appealing texture. In both stages, the 3D content is learned through
reference view supervision and novel views guided by a combination of 2D and 3D
diffusion priors. We introduce a single trade-off parameter between the 2D and
3D priors to control exploration (more imaginative) and exploitation (more
precise) of the generated geometry. Additionally, we employ textual inversion
and monocular depth regularization to encourage consistent appearances across
views and to prevent degenerate solutions, respectively. Magic123 demonstrates
a significant improvement over previous image-to-3D techniques, as validated
through extensive experiments on synthetic benchmarks and diverse real-world
images. Our code, models, and generated 3D assets are available at
https://github.com/guochengqian/Magic123.
