title: GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View
    Synthesis
summary: 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
paper_authors._id: 658136e0c15f9833028210c2
paper_authors.name: Yiqing Liang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-19T19:21:45.763Z
paper_authors._id: 6581d57b283fdb0c8dcb9851
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/elZhAKdRizAiAKtiAWBr3.png
paper_authors.fullname: Yiqing Liang
paper_authors.type: user
paper_authors.user: yiqingliang
paper_authors._id: 658136e0c15f9833028210c3
paper_authors.name: Numair Khan
paper_authors._id: 658136e0c15f9833028210c4
paper_authors.name: Zhengqin Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T10:13:58.731Z
paper_authors._id: 64ff5b2569219ce3e47893f1
paper_authors.avatarUrl: /avatars/36bfb3de85488c0efa75d741eac9c40e.svg
paper_authors.fullname: Li
paper_authors.type: user
paper_authors.user: Zhengqin
paper_authors._id: 658136e0c15f9833028210c5
paper_authors.name: Thu Nguyen-Phuoc
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T10:14:06.026Z
paper_authors._id: 6304b663c2f4f2d4929ad200
paper_authors.avatarUrl: /avatars/805d4dab512fc53d8bd6bcb60db3821b.svg
paper_authors.fullname: Thu Nguyen-Phuoc
paper_authors.type: user
paper_authors.user: TNP23
paper_authors._id: 658136e0c15f9833028210c6
paper_authors.name: Douglas Lanman
paper_authors._id: 658136e0c15f9833028210c7
paper_authors.name: James Tompkin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T15:50:09.353Z
paper_authors._id: 6581b07b4466994ea8491941
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6581b07b4466994ea8491941/dsKpoHwnyTqCaSRMV3evn.png
paper_authors.fullname: James Tompkin
paper_authors.type: user
paper_authors.user: jamestompkin
paper_authors._id: 658136e0c15f9833028210c8
paper_authors.name: Lei Xiao
paper_title: GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View
    Synthesis
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: 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