GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View
  Synthesis
We propose a method for dynamic scene reconstruction using deformable 3D
Gaussians that is tailored for monocular video. Building upon the efficiency of
Gaussian splatting, our approach extends the representation to accommodate
dynamic elements via a deformable set of Gaussians residing in a canonical
space, and a time-dependent deformation field defined by a multi-layer
perceptron (MLP). Moreover, under the assumption that most natural scenes have
large regions that remain static, we allow the MLP to focus its
representational power by additionally including a static Gaussian point cloud.
The concatenated dynamic and static point clouds form the input for the
Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable
pipeline is optimized end-to-end with a self-supervised rendering loss. Our
method achieves results that are comparable to state-of-the-art dynamic neural
radiance field methods while allowing much faster optimization and rendering.
Project website: https://lynl7130.github.io/gaufre/index.html
658136e0c15f9833028210c2
Yiqing Liang
claimed_verified
2023-12-19T19:21:45.763Z
6581d57b283fdb0c8dcb9851
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/elZhAKdRizAiAKtiAWBr3.png
Yiqing Liang
user
yiqingliang
658136e0c15f9833028210c3
Numair Khan
658136e0c15f9833028210c4
Zhengqin Li
admin_assigned
2023-12-19T10:13:58.731Z
64ff5b2569219ce3e47893f1
/avatars/36bfb3de85488c0efa75d741eac9c40e.svg
Li
user
Zhengqin
658136e0c15f9833028210c5
Thu Nguyen-Phuoc
admin_assigned
2023-12-19T10:14:06.026Z
6304b663c2f4f2d4929ad200
/avatars/805d4dab512fc53d8bd6bcb60db3821b.svg
Thu Nguyen-Phuoc
user
TNP23
658136e0c15f9833028210c6
Douglas Lanman
658136e0c15f9833028210c7
James Tompkin
admin_assigned
2023-12-19T15:50:09.353Z
6581b07b4466994ea8491941
https://cdn-avatars.huggingface.co/v1/production/uploads/6581b07b4466994ea8491941/dsKpoHwnyTqCaSRMV3evn.png
James Tompkin
user
jamestompkin
658136e0c15f9833028210c8
Lei Xiao
GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View
  Synthesis
We propose a method for dynamic scene reconstruction using deformable 3D
Gaussians that is tailored for monocular video. Building upon the efficiency of
Gaussian splatting, our approach extends the representation to accommodate
dynamic elements via a deformable set of Gaussians residing in a canonical
space, and a time-dependent deformation field defined by a multi-layer
perceptron (MLP). Moreover, under the assumption that most natural scenes have
large regions that remain static, we allow the MLP to focus its
representational power by additionally including a static Gaussian point cloud.
The concatenated dynamic and static point clouds form the input for the
Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable
pipeline is optimized end-to-end with a self-supervised rendering loss. Our
method achieves results that are comparable to state-of-the-art dynamic neural
radiance field methods while allowing much faster optimization and rendering.
Project website: https://lynl7130.github.io/gaufre/index.html
A method for dynamic scene reconstruction using deformable 3D Gaussians in monocular video leverages Gaussian splatting with a time-dependent deformation field and static Gaussian point cloud, enabling real-time rendering.
deformable 3D Gaussians Gaussian splatting monocular video MLP canonical space time-dependent deformation field static Gaussian point cloud differentiable pipeline self-supervised rendering loss dynamic neural radiance field