title: Advances in 3D Generation: A Survey
summary: Generating 3D models lies at the core of computer graphics and has been the
  focus of decades of research. With the emergence of advanced neural
  representations and generative models, the field of 3D content generation is
  developing rapidly, enabling the creation of increasingly high-quality and
  diverse 3D models. The rapid growth of this field makes it difficult to stay
  abreast of all recent developments. In this survey, we aim to introduce the
  fundamental methodologies of 3D generation methods and establish a structured
  roadmap, encompassing 3D representation, generation methods, datasets, and
  corresponding applications. Specifically, we introduce the 3D representations
  that serve as the backbone for 3D generation. Furthermore, we provide a
  comprehensive overview of the rapidly growing literature on generation methods,
  categorized by the type of algorithmic paradigms, including feedforward
  generation, optimization-based generation, procedural generation, and
  generative novel view synthesis. Lastly, we discuss available datasets,
  applications, and open challenges. We hope this survey will help readers
  explore this exciting topic and foster further advancements in the field of 3D
  content generation.
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paper_authors.name: Xiaoyu Li
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paper_authors.name: Weihao Cheng
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paper_authors.name: Yiming Gao
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paper_authors.fullname: Jingbo Zhang
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paper_authors.name: Zhihao Liang
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paper_authors.name: Jing Liao
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paper_authors.name: Yan-Pei Cao
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paper_authors.fullname: Yanpei Cao
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paper_authors.name: Ying Shan
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paper_title: Advances in 3D Generation: A Survey
paper_summary: Generating 3D models lies at the core of computer graphics and has been the
  focus of decades of research. With the emergence of advanced neural
  representations and generative models, the field of 3D content generation is
  developing rapidly, enabling the creation of increasingly high-quality and
  diverse 3D models. The rapid growth of this field makes it difficult to stay
  abreast of all recent developments. In this survey, we aim to introduce the
  fundamental methodologies of 3D generation methods and establish a structured
  roadmap, encompassing 3D representation, generation methods, datasets, and
  corresponding applications. Specifically, we introduce the 3D representations
  that serve as the backbone for 3D generation. Furthermore, we provide a
  comprehensive overview of the rapidly growing literature on generation methods,
  categorized by the type of algorithmic paradigms, including feedforward
  generation, optimization-based generation, procedural generation, and
  generative novel view synthesis. Lastly, we discuss available datasets,
  applications, and open challenges. We hope this survey will help readers
  explore this exciting topic and foster further advancements in the field of 3D
  content generation.