Google Genie 3 brings your 3D world to life with AI

On August 5th, 2025, Google DeepMind released its latest version of the Genie model, known as Genie 3. It is a new AI model that can convert a user’s text prompts into dynamic, interactive environments. 

These environments, or AI worlds, make it possible for the user to navigate and interact with them in real time, much like in a video game. Users can also expand or modify the environment by providing additional text prompts, enabling on-the-fly changes without restarting the simulation. 

What makes the latest Genie Google model particularly impactful is that it can be used to train AI agents. This involves teaching AI agents to make decisions or perform tasks using data and feedback. By using a simulated 3D environment instead of the real world, researchers can avoid many of the challenges, costs, and risks of real-world training.

Google Genie 3 can also simulate complex scenarios, such as testing an autonomous car driving through heavy weather or a wingsuit gliding through mountainous terrain. 

In this article, we’ll explore Google Genie 3 and its capabilities. Let’s get started!

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A brief history of Google’s Genie models

Before we dive into Google DeepMind’s Genie models, let’s get a better understanding of what world models are. 

World models are AI systems that learn real-world rules like physics, motion, and spatial relationships from text, images, videos, and movement datasets. This allows them to create realistic scenes and predict how they evolve. The Genie models are examples of such systems.

Here is a quick glimpse of the earlier Google Genie models that paved the way for Genie 3:

• Genie 1: Genie 1, often referred to simply as Google Genie, was Google DeepMind’s first AI world model capable of creating interactive virtual environments. Users could describe a world with text, images, photos, or even sketches, and Genie would generate it, letting them control actions within the scene. It was designed to process video data over time, predict the next frame, and translate user inputs into in-world actions.

• Genie 2: Building on the capabilities of Google Genie, Genie 2 could create a wide range of detailed, interactive 3D worlds. As a world model, it simulated virtual environments and responded realistically to actions such as jumping, swimming, or moving objects. Trained on a massive collection of videos, it featured realistic object interactions and lifelike character movements.

What is Genie 3? Google’s new AI model

Building on earlier Genie models, Genie 3 is the latest and most advanced in the series. It builds particularly on Genie 2, which could generate new virtual environments, and Veo 3, Google DeepMind’s latest video generation model. Veo 3 demonstrates a deep understanding of physics and how objects interact in the real world.

While Veo 3 uses a hard-coded physics engine, Google Genie 3 teaches itself how physics works using a method known as self-supervised learning. It is an AI learning technique where an AI model learns patterns and relationships from unlabeled data by generating its own learning signals. 

Google Genie 3’s self-supervised learning capability is crucial for training AI systems, such as AI agents or AI robots, to handle various tasks. In fact, researchers at Google DeepMind see Genie 3 as an important step towards the creation of Artificial General Intelligence (AGI). 

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AGI is a theoretical form of AI that can understand and learn any task or subject and apply that knowledge across different situations, much like a human. Unlike today’s artificial intelligence models, which are built for specific tasks and struggle to transfer their skills to new problems, AGI would be able to adapt and learn in a wide range of contexts.

Key features of Google Genie 3 related to building an AI world

Here are some of the key features supported by Genie 3:

• Text-to-3D world generation: It can turn a simple text prompt (e.g., “a robot walking down the street”) into a playable 3D-like environment with basic movement controls.

• Promptable world events: Users can dynamically change the environment by typing new commands (e.g., add rain to the street).

• Visual memory: Genie 3 can remember objects left behind in the environment and let you revisit them later, lasting for about one minute.

• Smooth and consistent video output: It can maintain a video output of 24 fps (frames per second) at 720p resolution, with longer engagement compared to Genie 2.

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Education to gaming: Applications of Google DeepMind’s Genie 3

Google Genie 3 can make learning, research, and training more immersive and engaging. For example, in classrooms, it can bring history, science, or geography to life by letting students explore ancient cities or travel through space. Similarly, for artificial intelligence developers, it offers realistic virtual worlds to practice strategies, navigate challenges, and improve decision-making skills.

Scientists can also use it to create controlled simulations for testing ideas, studying ecosystems, or observing the behavior of objects. Another interesting application is in video game development. Game developers can turn text prompts into detailed game worlds, speeding up development and reducing the need for large teams.

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Limitations of Google Genie 3 as a world model

While Google Genie 3 offers many features and benefits, it’s also important to consider its drawbacks. 

Here are some limitations to consider:

• Limited action range: While you can trigger many events in the virtual world, not all of them are carried out by the agent itself. The actions an agent can perform directly are still limited.

• Interacting with other agents: Creating realistic interactions between multiple independent agents in the same environment is still a work in progress.

• Real-world accuracy: Google Genie 3 can’t yet recreate real-world locations with perfect geographic precision.

Key takeaways

Google Genie 3 represents a significant advancement in creating realistic, interactive 3D worlds with AI. It can bring ideas to life from simple text prompts, simulate physics, and even train AI systems in safe virtual spaces. 

While it still has limits, it opens up many possibilities for research, gaming, and AI development. It’s also a crucial step toward AGI systems that can think and learn more like humans.

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