GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion
  Probabilistic Models with Structured Noise
Xinhai Li Huaibin Wang Kuo-Kun Tseng
Text-to-3D, known for its efficient generation methods and expansive creative
potential, has garnered significant attention in the AIGC domain. However, the
amalgamation of Nerf and 2D diffusion models frequently yields oversaturated
images, posing severe limitations on downstream industrial applications due to
the constraints of pixelwise rendering method. Gaussian splatting has recently
superseded the traditional pointwise sampling technique prevalent in NeRF-based
methodologies, revolutionizing various aspects of 3D reconstruction. This paper
introduces a novel text to 3D content generation framework based on Gaussian
splatting, enabling fine control over image saturation through individual
Gaussian sphere transparencies, thereby producing more realistic images. The
challenge of achieving multi-view consistency in 3D generation significantly
impedes modeling complexity and accuracy. Taking inspiration from SJC, we
explore employing multi-view noise distributions to perturb images generated by
3D Gaussian splatting, aiming to rectify inconsistencies in multi-view
geometry. We ingeniously devise an efficient method to generate noise that
produces Gaussian noise from diverse viewpoints, all originating from a shared
noise source. Furthermore, vanilla 3D Gaussian-based generation tends to trap
models in local minima, causing artifacts like floaters, burrs, or
proliferative elements. To mitigate these issues, we propose the variational
Gaussian splatting technique to enhance the quality and stability of 3D
appearance. To our knowledge, our approach represents the first comprehensive
utilization of Gaussian splatting across the entire spectrum of 3D content
generation processes.