title: DreamGaussian4D: Generative 4D Gaussian Splatting
summary: Remarkable progress has been made in 4D content generation recently. However,
  existing methods suffer from long optimization time, lack of motion
  controllability, and a low level of detail. In this paper, we introduce
  DreamGaussian4D, an efficient 4D generation framework that builds on 4D
  Gaussian Splatting representation. Our key insight is that the explicit
  modeling of spatial transformations in Gaussian Splatting makes it more
  suitable for the 4D generation setting compared with implicit representations.
  DreamGaussian4D reduces the optimization time from several hours to just a few
  minutes, allows flexible control of the generated 3D motion, and produces
  animated meshes that can be efficiently rendered in 3D engines.
paper_authors._id: 658e4a880f4519bfc22b03e4
paper_authors.name: Jiawei Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-29T09:00:16.800Z
paper_authors._id: 623f533a28672458f749b8e9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1651044799068-623f533a28672458f749b8e9.png
paper_authors.fullname: Jiawe Ren
paper_authors.type: user
paper_authors.user: jiawei011
paper_authors._id: 658e4a880f4519bfc22b03e5
paper_authors.name: Liang Pan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-05T16:12:59.138Z
paper_authors._id: 64ef30fb0de94d31b7921175
paper_authors.avatarUrl: /avatars/d4f4c7be460befc1cedec13bbf9db972.svg
paper_authors.fullname: pan
paper_authors.type: user
paper_authors.user: pldeqiushui
paper_authors._id: 658e4a880f4519bfc22b03e6
paper_authors.name: Jiaxiang Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T16:53:43.685Z
paper_authors._id: 6664dc00da1e6adefea0e177
paper_authors.avatarUrl: /avatars/17facee3d6feb800eee233ae81816cd5.svg
paper_authors.fullname: jiaxiang tang
paper_authors.type: user
paper_authors.user: jiaxiang-tang
paper_authors._id: 658e4a880f4519bfc22b03e7
paper_authors.name: Chi Zhang
paper_authors._id: 658e4a880f4519bfc22b03e8
paper_authors.name: Ang Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T16:54:23.066Z
paper_authors._id: 63a1c5dde36f2e4d5b0d9ca2
paper_authors.avatarUrl: /avatars/fd9adbada165f077427ef98af854a9f4.svg
paper_authors.fullname: Ang Cao
paper_authors.type: user
paper_authors.user: AACEE
paper_authors._id: 658e4a880f4519bfc22b03e9
paper_authors.name: Gang Zeng
paper_authors._id: 658e4a880f4519bfc22b03ea
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-10T16:54:37.572Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_title: DreamGaussian4D: Generative 4D Gaussian Splatting
paper_summary: Remarkable progress has been made in 4D content generation recently. However,
  existing methods suffer from long optimization time, lack of motion
  controllability, and a low level of detail. In this paper, we introduce
  DreamGaussian4D, an efficient 4D generation framework that builds on 4D
  Gaussian Splatting representation. Our key insight is that the explicit
  modeling of spatial transformations in Gaussian Splatting makes it more
  suitable for the 4D generation setting compared with implicit representations.
  DreamGaussian4D reduces the optimization time from several hours to just a few
  minutes, allows flexible control of the generated 3D motion, and produces
  animated meshes that can be efficiently rendered in 3D engines.
paper_ai_keywords: 4D content generation optimization time motion controllability DreamGaussian4D 4D generation framework 4D Gaussian Splatting representation spatial transformations Gaussian Splatting implicit representations animated meshes 3D engines