Gaia ML joins the AI GameDev Toolkit

[Adam]

Procedural Worlds, as an Intel® Software Innovator and world leader in procedural content generation, is pleased to announce that it will deliver a special FREE edition of Gaia called Gaia ML as part of Intel’s AI GameDev Toolkit.

The integration of Gaia ML into the AI GameDev Toolkit will make it easier and faster for game developers to create rich environments within which to test their AI creations.

Under the hood of this powerful new suite of tools resides the Intel® Distribution of OpenVINO™ toolkit, which simplifies the development process by helping target the right accelerators on the CPU, GPU, or both simultaneously. This enables game developers to harness the power of AI and machine learning with ease. It also leverages DP4a instructions in our latest integrated GPUs and is ready to take advantage of new XMX AI acceleration in our upcoming Intel® Arc™ graphics product family, too.

In addition, the AI GameDev Toolkit now enables you to apply generative adversarial networks (GANs) style transfer capabilities to stylize entire scenes or to focus on specific objects and characters within a scene, giving additional artistic flexibility.

One of the coolest new features the AI GameDev Toolkit brings to developers is the ability to unlock the potency of in-game object detection. Imagine enhancing your games to provide AI-assisted target identification, or providing mods that intelligently augment the game experience based on player preferences and learnings.

Extending this AI power into Procedural Worlds’ next-generation Meta Worlds platform promises to elevate game-world-creation capabilities to a previously unimaginable level. You can now create richly textured worlds that feature complex and photo-realistic terrains, lush vegetation, and lifelike environmental systems—in mere minutes.

The AI for GameDev Toolkit is available now, and Gaia ML is FREE!

Click here to learn more about and get Gaia ML.

Seamless Integration between Gaia ML and the AI GameDev Toolkit:

Style Transfer Example:

Object Detection Example: