title: GVGEN: Text-to-3D Generation with Volumetric Representation
summary: In recent years, 3D Gaussian splatting has emerged as a powerful technique
  for 3D reconstruction and generation, known for its fast and high-quality
  rendering capabilities. To address these shortcomings, this paper introduces a
  novel diffusion-based framework, GVGEN, designed to efficiently generate 3D
  Gaussian representations from text input. We propose two innovative
  techniques:(1) Structured Volumetric Representation. We first arrange
  disorganized 3D Gaussian points as a structured form GaussianVolume. This
  transformation allows the capture of intricate texture details within a volume
  composed of a fixed number of Gaussians. To better optimize the representation
  of these details, we propose a unique pruning and densifying method named the
  Candidate Pool Strategy, enhancing detail fidelity through selective
  optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the
  generation of GaussianVolume and empower the model to generate instances with
  detailed 3D geometry, we propose a coarse-to-fine pipeline. It initially
  constructs a basic geometric structure, followed by the prediction of complete
  Gaussian attributes. Our framework, GVGEN, demonstrates superior performance in
  qualitative and quantitative assessments compared to existing 3D generation
  methods. Simultaneously, it maintains a fast generation speed (sim7
  seconds), effectively striking a balance between quality and efficiency.
paper_authors._id: 65fa53fec11eeb751042e42d
paper_authors.name: Xianglong He
paper_authors._id: 65fa53fec11eeb751042e42e
paper_authors.name: Junyi Chen
paper_authors._id: 65fa53fec11eeb751042e42f
paper_authors.name: Sida Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:11:17.576Z
paper_authors._id: 62986ca2b58e71e2ac9b8f01
paper_authors.avatarUrl: /avatars/83944db5f3dbb6f47c47c46fb2cb2849.svg
paper_authors.fullname: Sida Peng
paper_authors.type: user
paper_authors.user: pengsida
paper_authors._id: 65fa53fec11eeb751042e430
paper_authors.name: Di Huang
paper_authors._id: 65fa53fec11eeb751042e431
paper_authors.name: Yangguang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-14T16:20:26.068Z
paper_authors._id: 64d71083a787c9bc7b9f1238
paper_authors.avatarUrl: /avatars/d0b0546dec7fc5792921154bec41385a.svg
paper_authors.fullname: Yangguang Li
paper_authors.type: user
paper_authors.user: Lp256
paper_authors._id: 65fa53fec11eeb751042e432
paper_authors.name: Xiaoshui Huang
paper_authors._id: 65fa53fec11eeb751042e433
paper_authors.name: Chun Yuan
paper_authors._id: 65fa53fec11eeb751042e434
paper_authors.name: Wanli Ouyang
paper_authors._id: 65fa53fec11eeb751042e435
paper_authors.name: Tong He
paper_title: GVGEN: Text-to-3D Generation with Volumetric Representation
paper_summary: In recent years, 3D Gaussian splatting has emerged as a powerful technique
  for 3D reconstruction and generation, known for its fast and high-quality
  rendering capabilities. To address these shortcomings, this paper introduces a
  novel diffusion-based framework, GVGEN, designed to efficiently generate 3D
  Gaussian representations from text input. We propose two innovative
  techniques:(1) Structured Volumetric Representation. We first arrange
  disorganized 3D Gaussian points as a structured form GaussianVolume. This
  transformation allows the capture of intricate texture details within a volume
  composed of a fixed number of Gaussians. To better optimize the representation
  of these details, we propose a unique pruning and densifying method named the
  Candidate Pool Strategy, enhancing detail fidelity through selective
  optimization. (2) Coarse-to-fine Generation Pipeline. To simplify the
  generation of GaussianVolume and empower the model to generate instances with
  detailed 3D geometry, we propose a coarse-to-fine pipeline. It initially
  constructs a basic geometric structure, followed by the prediction of complete
  Gaussian attributes. Our framework, GVGEN, demonstrates superior performance in
  qualitative and quantitative assessments compared to existing 3D generation
  methods. Simultaneously, it maintains a fast generation speed (sim7
  seconds), effectively striking a balance between quality and efficiency.
paper_ai_summary: A diffusion-based framework, GVGEN, uses a structured volumetric representation and a coarse-to-fine pipeline to generate high-quality 3D Gaussian representations from text with fast speed.
paper_ai_keywords: 3D Gaussian splatting diffusion-based framework GaussianVolume Candidate Pool Strategy coarse-to-fine pipeline