  The creation of high-quality 3D assets is paramount for applications in
digital heritage preservation, entertainment, and robotics. Traditionally, this
process necessitates skilled professionals and specialized software for the
modeling, texturing, and rendering of 3D objects. However, the rising demand
for 3D assets in gaming and virtual reality (VR) has led to the creation of
accessible image-to-3D technologies, allowing non-professionals to produce 3D
content and decreasing dependence on expert input. Existing methods for 3D
content generation struggle to simultaneously achieve detailed textures and
strong geometric consistency. We introduce a novel 3D content creation
framework, ScalingGaussian, which combines 3D and 2D diffusion models to
achieve detailed textures and geometric consistency in generated 3D assets.
Initially, a 3D diffusion model generates point clouds, which are then
densified through a process of selecting local regions, introducing Gaussian
noise, followed by using local density-weighted selection. To refine the 3D
gaussians, we utilize a 2D diffusion model with Score Distillation Sampling
(SDS) loss, guiding the 3D Gaussians to clone and split. Finally, the 3D
Gaussians are converted into meshes, and the surface textures are optimized
using Mean Square Error(MSE) and Gradient Profile Prior(GPP) losses. Our method
addresses the common issue of sparse point clouds in 3D diffusion, resulting in
improved geometric structure and detailed textures. Experiments on image-to-3D
tasks demonstrate that our approach efficiently generates high-quality 3D
assets.
