AGG: Amortized Generative 3D Gaussians for Single Image to 3D
Dejia Xu Ye Yuan Morteza Mardani Sifei Liu Jiaming Song Zhangyang Wang 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/