title: GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly
    Enhanced Quality
summary: Recently, 3D Gaussian splatting (3D-GS) has achieved great success in
  reconstructing and rendering real-world scenes. To transfer the high rendering
  quality to generation tasks, a series of research works attempt to generate
  3D-Gaussian assets from text. However, the generated assets have not achieved
  the same quality as those in reconstruction tasks. We observe that Gaussians
  tend to grow without control as the generation process may cause indeterminacy.
  Aiming at highly enhancing the generation quality, we propose a novel framework
  named GaussianDreamerPro. The main idea is to bind Gaussians to reasonable
  geometry, which evolves over the whole generation process. Along different
  stages of our framework, both the geometry and appearance can be enriched
  progressively. The final output asset is constructed with 3D Gaussians bound to
  mesh, which shows significantly enhanced details and quality compared with
  previous methods. Notably, the generated asset can also be seamlessly
  integrated into downstream manipulation pipelines, e.g. animation, composition,
  and simulation etc., greatly promoting its potential in wide applications.
  Demos are available at https://taoranyi.com/gaussiandreamerpro/.
paper_authors._id: 667e60c9b0f62e5e077f41d4
paper_authors.name: Taoran Yi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-01T07:46:59.957Z
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paper_authors.fullname: taoranyi
paper_authors.type: user
paper_authors.user: thewhole
paper_authors._id: 667e60c9b0f62e5e077f41d5
paper_authors.name: Jiemin Fang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-01T09:31:05.128Z
paper_authors._id: 652cf49cc6857682d3168f5a
paper_authors.avatarUrl: /avatars/9ccffa04832429aad37320a301ea8e36.svg
paper_authors.fullname: Jiemin Fang
paper_authors.type: user
paper_authors.user: JieminFang
paper_authors._id: 667e60c9b0f62e5e077f41d6
paper_authors.name: Zanwei Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-01T09:31:13.566Z
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paper_authors.avatarUrl: /avatars/ad28bc90a3820265501b95bfa5bb9418.svg
paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Zanwei
paper_authors._id: 667e60c9b0f62e5e077f41d7
paper_authors.name: Junjie Wang
paper_authors._id: 667e60c9b0f62e5e077f41d8
paper_authors.name: Guanjun Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-01T09:31:38.299Z
paper_authors._id: 65297efc802e3d1a4f7e23e8
paper_authors.avatarUrl: /avatars/09dc58dbbce5684e9edcb813bf078cc9.svg
paper_authors.fullname: Guanjun Wu
paper_authors.type: user
paper_authors.user: SnowGeraltOfRivia
paper_authors._id: 667e60c9b0f62e5e077f41d9
paper_authors.name: Lingxi Xie
paper_authors._id: 667e60c9b0f62e5e077f41da
paper_authors.name: Xiaopeng Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-01T09:31:52.464Z
paper_authors._id: 640bea9cb4f73086cddbe0a6
paper_authors.avatarUrl: /avatars/ae460dd971de1d04660b266b22f81f9d.svg
paper_authors.fullname: Xiaopeng Zhang
paper_authors.type: user
paper_authors.user: xpzhang
paper_authors._id: 667e60c9b0f62e5e077f41db
paper_authors.name: Wenyu Liu
paper_authors._id: 667e60c9b0f62e5e077f41dc
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-01T09:32:34.158Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_authors._id: 667e60c9b0f62e5e077f41dd
paper_authors.name: Qi Tian
paper_title: GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly
    Enhanced Quality
paper_summary: Recently, 3D Gaussian splatting (3D-GS) has achieved great success in
  reconstructing and rendering real-world scenes. To transfer the high rendering
  quality to generation tasks, a series of research works attempt to generate
  3D-Gaussian assets from text. However, the generated assets have not achieved
  the same quality as those in reconstruction tasks. We observe that Gaussians
  tend to grow without control as the generation process may cause indeterminacy.
  Aiming at highly enhancing the generation quality, we propose a novel framework
  named GaussianDreamerPro. The main idea is to bind Gaussians to reasonable
  geometry, which evolves over the whole generation process. Along different
  stages of our framework, both the geometry and appearance can be enriched
  progressively. The final output asset is constructed with 3D Gaussians bound to
  mesh, which shows significantly enhanced details and quality compared with
  previous methods. Notably, the generated asset can also be seamlessly
  integrated into downstream manipulation pipelines, e.g. animation, composition,
  and simulation etc., greatly promoting its potential in wide applications.
  Demos are available at https://taoranyi.com/gaussiandreamerpro/.
paper_ai_keywords: 3D Gaussian splatting GaussianDreamerPro Gaussians geometry appearance 3D Gaussians bound to mesh details quality animation composition simulation