Insights into the methods, datasets, and applications



summary
Summary

A new survey paper provides an in-depth look at the methods, datasets, and applications of how artificial intelligence could fundamentally change 3D development.

3D modeling has gained many new capabilities through the use of neural representations and generative AI models.

A new survey paper provides a structured insight into the underlying methods, datasets, and applications in the field of AI for 3D content generation and editing.

What’s striking is the enormous variety and complexity of methods and techniques that have emerged in a very short time. The authors speak of “explosive growth,” with new developments occurring on a weekly or even daily basis.

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They divide the field into four main areas:

  • 3D representations,
  • generation methods,
  • datasets
  • and various applications.
GAN and diffusion models currently dominate 3D generation. | Image: Li et al.

According to the authors, 3D generation can be useful in nearly all areas, in obvious applications such as games and virtual reality, but also in film or robotics.

Applications range from the generation of virtual people and realistic faces to individual 3D objects or entire 3D scenes.

Overview of 3D generation methods. | Image: Li et al.

In addition to 3D generation, the paper also discusses 3D editing tasks, which can be divided into global and local editing.

Global editing aims to change the appearance or geometry of the entire 3D scene. Local editing focuses on changing a specific area of a scene or object.

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