title: Text-to-3D using Gaussian Splatting
summary: In this paper, we present Gaussian Splatting based text-to-3D generation
  (GSGEN), a novel approach for generating high-quality 3D objects. Previous
  methods suffer from inaccurate geometry and limited fidelity due to the absence
  of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
  recent state-of-the-art representation, to address existing shortcomings by
  exploiting the explicit nature that enables the incorporation of 3D prior.
  Specifically, our method adopts a progressive optimization strategy, which
  includes a geometry optimization stage and an appearance refinement stage. In
  geometry optimization, a coarse representation is established under a 3D
  geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
  3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
  iterative refinement to enrich details. In this stage, we increase the number
  of Gaussians by compactness-based densification to enhance continuity and
  improve fidelity. With these designs, our approach can generate 3D content with
  delicate details and more accurate geometry. Extensive evaluations demonstrate
  the effectiveness of our method, especially for capturing high-frequency
  components. Video results are provided at https://gsgen3d.github.io. Our code
  is available at https://github.com/gsgen3d/gsgen
paper_authors._id: 6516483aa73c1d264877b945
paper_authors.name: Zilong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-29T11:47:42.726Z
paper_authors._id: 644e3e5f030210812f413073
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/uW8TKV2sds97lFBwnt6JK.jpeg
paper_authors.fullname: Zilong Chen
paper_authors.type: user
paper_authors.user: heheyas
paper_authors._id: 6516483aa73c1d264877b946
paper_authors.name: Feng Wang
paper_authors._id: 6516483aa73c1d264877b947
paper_authors.name: Huaping Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-29T11:47:33.685Z
paper_authors._id: 63cb64351b705cc951e98fc8
paper_authors.avatarUrl: /avatars/eea059d9295767d7695b5a4d5ff8f53c.svg
paper_authors.fullname: Liu
paper_authors.type: user
paper_authors.user: Huaping
paper_title: Text-to-3D using Gaussian Splatting
paper_summary: In this paper, we present Gaussian Splatting based text-to-3D generation
  (GSGEN), a novel approach for generating high-quality 3D objects. Previous
  methods suffer from inaccurate geometry and limited fidelity due to the absence
  of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
  recent state-of-the-art representation, to address existing shortcomings by
  exploiting the explicit nature that enables the incorporation of 3D prior.
  Specifically, our method adopts a progressive optimization strategy, which
  includes a geometry optimization stage and an appearance refinement stage. In
  geometry optimization, a coarse representation is established under a 3D
  geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
  3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
  iterative refinement to enrich details. In this stage, we increase the number
  of Gaussians by compactness-based densification to enhance continuity and
  improve fidelity. With these designs, our approach can generate 3D content with
  delicate details and more accurate geometry. Extensive evaluations demonstrate
  the effectiveness of our method, especially for capturing high-frequency
  components. Video results are provided at https://gsgen3d.github.io. Our code
  is available at https://github.com/gsgen3d/gsgen
paper_ai_keywords: Gaussian Splatting GSGEN 3D prior 3D Gaussian Splatting progressive optimization strategy geometry optimization stage appearance refinement stage 3D geometry prior 2D SDS loss 3D-consistent Gaussians compactness-based densification high-frequency components