Three Gen is specifically built to address the challenges faced by industries such as gaming, film, VFX, and digital twins when creating 3D content. This subnet uses a novel AI-driven approach, combining neural networks and Gaussian splats, a more efficient alternative to traditional 3D rendering techniques like meshes and neural radiance fields (nerfs).

• Gaussian splats provide a lightweight and scalable method for representing 3D objects, ideal for high-fidelity reconstructions without the heavy computational load typical of traditional methods.
• The system allows users to generate complex 3D models, from simple objects to entire virtual environments, quickly and efficiently.
• The technology is designed for integration into major 3D content creation pipelines, like Unreal Engine and Unity, to help indie developers and large studios alike.

The subnet aims to democratize content creation by providing high-quality, AI-generated 3D assets at a fraction of the cost and time it would traditionally take, making it accessible to both large AAA studios and indie game developers.

Early Development & Foundation:

• The company began with research funding and aimed to revolutionize 3D content creation for the gaming industry, primarily targeting larger AAA game developers. They were working on improving the efficiency of 3D world-building through the use of technologies like image processing and computer vision.
• The main shift happened when they discovered Bit Tensor and realized it offered a powerful platform for democratizing content creation, especially for indie game developers and smaller studios. This led to the creation of the subnet, which would be built on the Bit Tensor framework, enabling fast, efficient 3D content generation through AI.

Transition to Bit Tensor & Incentive Design:

• The subnet was built with a focus on 3D AI, leveraging the growing use cases in industries such as gaming, digital twins, and metaverses.
• The primary innovation of the subnet lies in its incentive design, encouraging miners to create the best AI models for 3D representation. This incentive structure, unique to Bit Tensor, allows miners to contribute to the growing ecosystem by developing high-quality, scalable 3D models.
• Iterative Development: After about a year of being live on mainnet, the subnet has gone through many iterations based on user feedback and usage. The network continuously learns and improves, adapting to changes in the AI and 3D space.

Core Technology and Use Cases:

• The subnet specializes in creating 3D models using Gaussian splats—a new, more efficient form of 3D representation that provides the high-fidelity and detail required for games, metaverses, and virtual environments, but without the computational expense of traditional meshes.
• One of the driving ideas was to build an intelligent subnet that works within the fragmented 3D AI landscape. While other methods like meshes and nerfs are currently in use, there’s no dominant solution, which allows miners to experiment with different approaches and contribute to a rapidly evolving space.
• In terms of application, the subnet is particularly well-suited for use cases in gaming, VFX, digital twins, and even architecture. For example, it can significantly reduce the time and cost associated with creating detailed 3D objects in virtual worlds.

Third-Party Integration and Open-Source Data:

• The subnet supports third-party developers and artists by making the generated 3D models available through APIs, and integrating them into popular 3D platforms like Unreal Engine and Unity. This allows for easy adoption by both indie game developers and larger studios.
• One of the key achievements was the creation of an open-source dataset that is now the largest in its field, surpassing all other 3D model datasets combined. This provides valuable resources for AI research and development in 3D space.
• The team has also contributed plugins for Unity and Unreal Engine, enabling users to integrate Gaussian splats directly into their existing pipelines.

Refining the Technology & Expanding the Reach:

• The subnet’s validation mechanism has been evolving to handle the complexity of determining model quality. The current system uses ELO rankings to assess model quality through head-to-head battles, ensuring that miners are rewarded for producing the highest-quality models.
• The next iteration of the validation system will balance fast validation (quick but less computationally expensive) with slow validation (more computationally intensive but more accurate).
• The subnet’s goal also includes expanding user-based features, like the ability to input 2D images to generate 3D models. This feature is being tested on the subnet’s testnet and will allow developers to generate 3D models from photographs, further streamlining the process.

Widening Applications and Industry Adoption:

• The subnet is starting to see traction in other industries beyond gaming. There are active projects in architecture, film VFX, and metaverses, proving that the technology has far-reaching potential.
• Projects from the subnet have been displayed at prestigious events, such as the Venice Architecture Biennale and the Osaka World Expo, showcasing how the technology is being used for cutting-edge, AI-generated 3D art and virtual worlds.
• Unity verification: The subnet became a verified solution for Unity, which is a significant accomplishment as Unity is one of the most popular platforms in the gaming industry. This verification indicates that the subnet’s technology has passed extensive tech due diligence and meets industry standards.

Future Outlook and Community Engagement:

• The subnet aims to continue improving its tools and features, with an emphasis on user feedback. With the ongoing development of AI technologies and new applications in gaming, architecture, and digital twins, the team is committed to keeping up with the evolving landscape.
• The community plays a crucial role in shaping the future of the subnet, with users constantly testing new features, submitting feedback, and integrating the technology into their projects.
• The long-term goal is to make the subnet’s AI-driven 3D generation tools accessible to a wider audience, not just in Web 3.0 but across mainstream industries like gaming, architecture, VFX, and more.