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Text-to-3D, known for its efficient generation methods and expansive creativepotential, has garnered significant attention in the AIGC domain. However, theamalgamation of Nerf and 2D diffusion models frequently yields oversaturatedimages, posing severe limitations on downstream industrial applications due tothe constraints of pixelwise rendering method. Gaussian splatting has recentlysuperseded the traditional pointwise sampling technique prevalent in NeRF-basedmethodologies, revolutionizing various aspects of 3D reconstruction. This paperintroduces a novel text to 3D content generation framework based on Gaussiansplatting, enabling fine control over image saturation through individualGaussian sphere transparencies, thereby producing more realistic images. Thechallenge of achieving multi-view consistency in 3D generation significantlyimpedes modeling complexity and accuracy. Taking inspiration from SJC, weexplore employing multi-view noise distributions to perturb images generated by3D Gaussian splatting, aiming to rectify inconsistencies in multi-viewgeometry. We ingeniously devise an efficient method to generate noise thatproduces Gaussian noise from diverse viewpoints, all originating from a sharednoise source. Furthermore, vanilla 3D Gaussian-based generation tends to trapmodels in local minima, causing artifacts like floaters, burrs, orproliferative elements. To mitigate these issues, we propose the variationalGaussian splatting technique to enhance the quality and stability of 3Dappearance. To our knowledge, our approach represents the first comprehensiveutilization of Gaussian splatting across the entire spectrum of 3D contentgeneration processes.

GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise