PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation

Recent advances in 3D content generation from text or im-ages have achieved impressive results, yet view inconsistency from 2Dgenerators and the scarcity of high-quality 3D data remain significantbottlenecks. Existing solutions [21, 28] typically adapt large-scale pre-trained text-to-image latent diffusion models to generate 3D GaussianSplats (3DGS). However, these approaches often rely on training complexcascade pipelines that are computationally expensive and scalability-limited. Most critically, the quality of generated 3D assets is inherentlyconstrained by each component capacity and compressed latent space,leading to decoding artifacts and accumulated errors. To address theselimitations, we propose PixGS, a single-stage pipeline for direct high-quality 3DGS generation, which leverages recent advances in pixel-spacediffusion to bypass lossy latent compression while still benefiting from thevast 2D generative priors. By directly denoising 3D Gaussian attributesat each timestep, our method enables precise, splat-level regularizationof both appearance and geometry. Furthermore, we introduce a compre-hensive supervision strategy that incorporates surface normals, depth,and high-frequency structural information, which is often overlooked inprior works. Experiments demonstrate that PixGS outperforms currentstate-of-the-art methods while maintaining a fast inference speed (≈ 1son a single A100 GPU), offering a robust and efficient alternative tomulti-stage generation pipelines.