Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
Recent advancements in dynamic 3D scene reconstruction have shown promising
results, enabling high-fidelity 3D novel view synthesis with improved temporal
consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an
appealing approach due to its ability to model high-fidelity spatial and
temporal variations. However, existing methods suffer from substantial
computational and memory overhead due to the redundant allocation of 4D
Gaussians to static regions, which can also degrade image quality. In this
work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework
that adaptively represents static regions with 3D Gaussians while reserving 4D
Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian
representation and iteratively converts temporally invariant Gaussians into 3D,
significantly reducing the number of parameters and improving computational
efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation,
capturing complex motions with high fidelity. Our approach achieves
significantly faster training times compared to baseline 4D Gaussian Splatting
methods while maintaining or improving the visual quality.
682bedb2fdfa3c5de0e86a0d
Seungjun Oh
claimed_verified
2025-05-20T07:20:43.581Z
672b66744efad666d2efb0c8
/avatars/9c00a67e9d5b74694759849cca32b015.svg
Oh Seungjun
user
ohseungjun
682bedb2fdfa3c5de0e86a0e
Younggeun Lee
admin_assigned
2025-05-20T08:28:33.215Z
66a4a1a7d8e85b03deddfa59
/avatars/56dbec2101717ad9471e08a03ae51f0c.svg
Young geun Lee
user
LeeYG
682bedb2fdfa3c5de0e86a0f
Hyejin Jeon
admin_assigned
2025-05-20T08:28:47.259Z
64c0d2f962983511b95c38d6
/avatars/68d9d3002d7f5d39aa9a7e2a49d25532.svg
JeonHyejin
user
Heyjin
682bedb2fdfa3c5de0e86a10
Eunbyung Park
admin_assigned
2025-05-20T08:28:53.220Z
655e0141d36a195f663ee4b0
/avatars/97bb695ccefdcb2139b94bcae808cf99.svg
Eunbyung Park
user
epark
Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
Recent advancements in dynamic 3D scene reconstruction have shown promising
results, enabling high-fidelity 3D novel view synthesis with improved temporal
consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an
appealing approach due to its ability to model high-fidelity spatial and
temporal variations. However, existing methods suffer from substantial
computational and memory overhead due to the redundant allocation of 4D
Gaussians to static regions, which can also degrade image quality. In this
work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework
that adaptively represents static regions with 3D Gaussians while reserving 4D
Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian
representation and iteratively converts temporally invariant Gaussians into 3D,
significantly reducing the number of parameters and improving computational
efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation,
capturing complex motions with high fidelity. Our approach achieves
significantly faster training times compared to baseline 4D Gaussian Splatting
methods while maintaining or improving the visual quality.
A new framework, 3D-4D Gaussian Splatting, improves computational efficiency in dynamic 3D scene reconstruction by adaptively representing static and dynamic regions, reducing parameter count and training time.
4D Gaussian Splatting 4DGS 3D-4D Gaussian Splatting Gaussians 3D Gaussians 4D Gaussians temporal consistency high-fidelity computational efficiency dynamic elements temporal invariant