AGG: Amortized Generative 3D Gaussians for Single Image to 3D
Authors: name: Dejia Xu, name: Ye Yuan, name: Morteza Mardani, name: Sifei Liu, name: Jiaming Song, name: Zhangyang Wang, name: Arash Vahdat

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/