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

翻译：随着自动三维内容创建管线的需求日益增长，研究者们探索了多种三维表示方法，旨在从单张图像生成三维物体。凭借出色的渲染效率，基于三维高斯泼溅的模型近年来在三维重建与生成领域均表现优异。现有基于三维高斯泼溅的图像到三维生成方法通常依赖优化过程，需要大量计算代价高昂的分数蒸馏步骤。为应对这些挑战，我们提出了一种摊还生成式三维高斯框架（AGG），该框架能够从单张图像瞬时生成三维高斯体，无需逐实例优化。通过利用中间混合表示，AGG将三维高斯位置及其它外观属性的生成进行解耦，实现联合优化。此外，我们设计了一种级联管线：首先生成三维数据的粗略表示，随后通过三维高斯超分辨率模块进行上采样。在与现有基于优化的三维高斯框架及采用其他三维表示的采样型管线的对比评估中，AGG在定性与定量方面均展现出具有竞争力的生成能力，同时速度提升数个数量级。项目页面：https://ir1d.github.io/AGG/