AGG: Amortized Generative 3D Gaussians for Single Image to 3D
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/
659ccde599aeb9b5de3a65e3
Dejia Xu
admin_assigned
2024-01-09T09:30:01.342Z
62f687a0c58915315c4ff75d
/avatars/b657180c7666735062782edd4f6a69c9.svg
Dejia Xu
user
ir1d
659ccde599aeb9b5de3a65e4
Ye Yuan
659ccde599aeb9b5de3a65e5
Morteza Mardani
admin_assigned
2024-01-09T09:30:30.967Z
6350842a0f376d3c482bc04a
/avatars/dca5f6b3e867ed28f80882ea2b32c2c8.svg
morteza mardani
user
mortezamardani
659ccde599aeb9b5de3a65e6
Sifei Liu
admin_assigned
2024-01-09T09:30:58.222Z
62fab69f8cd542e895bafd6e
/avatars/c553bff4bd52b9a4f79e9c76fa22e27e.svg
Sifei Liu
user
zwrq
659ccde599aeb9b5de3a65e7
Jiaming Song
admin_assigned
2024-01-09T09:31:05.712Z
6312c2f4bbaa385279d2da1b
/avatars/2bf162889fa726ed18cc205b3f28609e.svg
Jiaming Song
user
jiamings
659ccde599aeb9b5de3a65e8
Zhangyang Wang
659ccde599aeb9b5de3a65e9
Arash Vahdat
admin_assigned
2024-01-09T09:31:39.970Z
62f6956cd3bdacb7eec02920
/avatars/b22db0823311f866c00db2efc4b9f814.svg
Arash Vahdat
user
avahdat
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
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/
An Amortized Generative 3D Gaussian framework (AGG) efficiently generates 3D Gaussians from a single image, offering competitive performance and speed advantages over existing methods.
3D Gaussian splatting Amortized Generative 3D Gaussian framework score-distillation intermediate hybrid representation cascaded pipeline Gaussian super-resolution module