title: DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content
    Creation
summary: Recent advances in 3D content creation mostly leverage optimization-based 3D
  generation via score distillation sampling (SDS). Though promising results have
  been exhibited, these methods often suffer from slow per-sample optimization,
  limiting their practical usage. In this paper, we propose DreamGaussian, a
  novel 3D content generation framework that achieves both efficiency and quality
  simultaneously. Our key insight is to design a generative 3D Gaussian Splatting
  model with companioned mesh extraction and texture refinement in UV space. In
  contrast to the occupancy pruning used in Neural Radiance Fields, we
  demonstrate that the progressive densification of 3D Gaussians converges
  significantly faster for 3D generative tasks. To further enhance the texture
  quality and facilitate downstream applications, we introduce an efficient
  algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning
  stage to refine the details. Extensive experiments demonstrate the superior
  efficiency and competitive generation quality of our proposed approach.
  Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes
  from a single-view image, achieving approximately 10 times acceleration
  compared to existing methods.
paper_authors._id: 65164b016137c31730198cda
paper_authors.name: Jiaxiang Tang
paper_authors._id: 65164b016137c31730198cdb
paper_authors.name: Jiawei Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-29T07:04:18.963Z
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: 65164b016137c31730198cdc
paper_authors.name: Hang Zhou
paper_authors._id: 65164b016137c31730198cdd
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-29T07:07:58.041Z
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_authors._id: 65164b016137c31730198cde
paper_authors.name: Gang Zeng
paper_title: DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content
    Creation
paper_summary: Recent advances in 3D content creation mostly leverage optimization-based 3D
  generation via score distillation sampling (SDS). Though promising results have
  been exhibited, these methods often suffer from slow per-sample optimization,
  limiting their practical usage. In this paper, we propose DreamGaussian, a
  novel 3D content generation framework that achieves both efficiency and quality
  simultaneously. Our key insight is to design a generative 3D Gaussian Splatting
  model with companioned mesh extraction and texture refinement in UV space. In
  contrast to the occupancy pruning used in Neural Radiance Fields, we
  demonstrate that the progressive densification of 3D Gaussians converges
  significantly faster for 3D generative tasks. To further enhance the texture
  quality and facilitate downstream applications, we introduce an efficient
  algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning
  stage to refine the details. Extensive experiments demonstrate the superior
  efficiency and competitive generation quality of our proposed approach.
  Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes
  from a single-view image, achieving approximately 10 times acceleration
  compared to existing methods.
paper_ai_keywords: score distillation sampling (SDS) generative 3D Gaussian Splatting companioned mesh extraction texture refinement occupancy pruning Neural Radiance Fields progressive densification 3D Gaussians textured meshes fine-tuning stage