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Given the growing need for automatic 3D content creation pipelines, various3D representations have been studied to generate 3D objects from a singleimage. Due to its superior rendering efficiency, 3D Gaussian splatting-basedmodels have recently excelled in both 3D reconstruction and generation. 3DGaussian splatting approaches for image to 3D generation are oftenoptimization-based, requiring many computationally expensive score-distillationsteps. To overcome these challenges, we introduce an Amortized Generative 3DGaussian framework (AGG) that instantly produces 3D Gaussians from a singleimage, eliminating the need for per-instance optimization. Utilizing anintermediate hybrid representation, AGG decomposes the generation of 3DGaussian locations and other appearance attributes for joint optimization.Moreover, we propose a cascaded pipeline that first generates a coarserepresentation of the 3D data and later upsamples it with a 3D Gaussiansuper-resolution module. Our method is evaluated against existingoptimization-based 3D Gaussian frameworks and sampling-based pipelinesutilizing other 3D representations, where AGG showcases competitive generationabilities both qualitatively and quantitatively while being several orders ofmagnitude faster. Project page: https://ir1d.github.io/AGG/

AGG: Amortized Generative 3D Gaussians for Single Image to 3D