  Single image-to-3D generation is pivotal for crafting controllable 3D assets.
Given its underconstrained nature, we leverage geometric priors from a 3D novel
view generation diffusion model and appearance priors from a 2D image
generation method to guide the optimization process. We note that a disparity
exists between the training datasets of 2D and 3D diffusion models, leading to
their outputs showing marked differences in appearance. Specifically, 2D models
tend to deliver more detailed visuals, whereas 3D models produce consistent yet
over-smooth results across different views. Hence, we optimize a set of 3D
Gaussians using 3D priors in spatial domain to ensure geometric consistency,
while exploiting 2D priors in the frequency domain through Fourier transform
for higher visual quality. This 2D-3D hybrid Fourier Score Distillation
objective function (dubbed hy-FSD), can be integrated into existing 3D
generation methods, yielding significant performance improvements. With this
technique, we further develop an image-to-3D generation pipeline to create
high-quality 3D objects within one minute, named Fourier123. Extensive
experiments demonstrate that Fourier123 excels in efficient generation with
rapid convergence speed and visual-friendly generation results.
