One of the most promising technologies to emerge in recent years is generative artificial intelligence (AI). With the ability to create images and 3D models, this technology has many applications and is becoming increasingly common in a wide range of industries. In this article, we introduce the world of generative AI in the field of image and 3D modeling and describe the main widely used approaches.

A branch of artificial intelligence known as "generative AI" is concerned with generating new data from previously collected information. In the field of image generation, generative AIs are often used to create realistic images of objects or landscapes. In the field of 3D modeling, they can be used to create intricate models of structures, machines, and other objects.

"AI-assisted Artistry"

There are various methods that generative AIs can use for 3D modeling and image generation. One of the best known approaches is the so-called "Generative Adversarial Network" (GAN). It is important to note here that GANs are a type of neural network consisting of two competing networks. One network generates images or 3D models, while another network attempts to identify those images or models and determine whether they are real or artificial. Then the generation network is trained to get better and better and try to outperform the separation network. GANs have proven to be quite effective and are widely used in industry.

Different approaches for content generation

Der „Variational Autoencoder“-Ansatz (VAE) ist eine weitere Strategie. Neuronale Netze, die versuchen zu lernen, wie Daten in einem bestimmten Raum zu verteilen sind, bilden VAEs. Diese Methode wird häufig bei der Bilderzeugung eingesetzt, da sie aus einer kleinen Anzahl von Beispielbildern ein Modell erstellt, das neue, verwandte Bilder erzeugen kann. Der VAE-Ansatz ist sehr hilfreich, wenn nur wenige Daten zur Verfügung stehen.

The "Neural Style Transfer" (NST) approach is another method for image generation. This is a technique for transferring the style of one image to another. This method is often used in the creation of artwork by transferring the style of famous artists such as Van Gogh or Picasso to any image.

Even the generation of 3D models is possible

There are also different approaches in the field of 3D modeling. The point cloud completion (PCC) approach is a common technique. The goal of PCC is to automatically add missing parts to 3D models by extending the model based on existing data. Another approach is the "mesh generation" (MG)-An approach, which creates a mesh of triangles to describe the 3D model. The MG-An approach is already being used to create 3D models for computer games or simulations, although it is still in its infancy.

Der Einsatz von generativen KIs im Bereich der 3D-Modellierung ist auch in der Architektur möglich. Sie können beispielsweise zur automatischen Erstellung realistischer 3D-Modelle von Gebäuden verwendet werden, die dann für die Planung und Visualisierung von Architekturprojekten genutzt werden können. Dies spart Zeit und Geld bei der manuellen Erstellung von 3D-Modellen.

Image sources: Midjourney,

"A 3D model of a cat" as imagined by Midjourney


Overall, generative AIs offer a wide range of benefits and potential applications in the areas of image generation and 3D modeling. The various approaches we have discussed in this article show how this technology can be used to create high-quality images and 3D models. It can be deduced that generative AIs will play an increasingly important role in industry in the future.

At the same time, we assume that classical artists will still keep their jobs. AI will not destroy jobs, but create new jobs for users who know how to use it as a tool.

The legal component is also still open: many algorithms have been trained on content that its authors have not voluntarily released for processing. It remains to be seen how the law will develop in this area.