After decades of speculation, machine learning and AI have finally taken center stage for their ability to empower users and workflows across industries. In the world of computer graphics, AI is already making waves and is set to change every corner of creating computer generated images. From creative inspiration, to managing massive data and rendering tasks, we’ll be looking at how machine learning is changing 3d workflows and cloud rendering for the better.

Machine Learning and Rendering

Traditionally, a render farm has been a purely power-driven service. But with the growth of AI, this is changing. Today, cloud render farm tools are beginning to implement AI-driven tools for things like resource allocation, automation, and render upscaling. Allocating resources efficiently for rendering is essential for keeping render costs low and maintaining render deadlines. Machine learning tools and automators are already being used by major server and render farm providers by intelligently determining the needs for a given project and pushing more resources to the projects that need it.

But this represents just the beginning for how machine learning could revolutionize computer graphics. Apart from render farm optimization tools, AI is being integrated into nearly every part of the 3d rendering pipeline.

The AI Assistant Art-Director

As the film and creative industry has become increasingly globalized, it’s become more difficult than ever for 3d artists to develop unique and inspired styles and designs. That’s where AI tools can step in, offering new styles, variations, and other suggestions to artists facing creative, or technical blocks.  For example, Dream Textures, was released late last year bringing the ability to generate prompt-based textures and materials directly inside the 3d software Blender. And companies like Adobe and 3DFY.ai are working on machine learning models to generate entire 3D assets from simple word prompts. Even though tools like these aren’t yet perfect, they are already empowering artists to move beyond their technical and creative limitations.

AI tools, be it for texture generation, 3d asset creation, or for full 3d renders, are all part of a new variety of AI-driven art direction. This kind of art-direction grants AI the role of a creative collaborator, proposing visual styles, variations, and layouts for artists to consider and integrate as they desire. Furthermore, as machine learning advances, the output from such AI tools could be good enough to realize visuals that an individual artist would never normally be capable of producing on their own. Artists could have the ability to quickly populate a 3d scene with Hollywood-level visuals without needing to spend hours, or weeks on creating or finding assets themselves.

This leads to the last major step in AI’s future for computer graphics: self-rendering. AI models that can generate full 3d renders and animations are already making waves in the industry. For example, Method Studios recently used AI to create the title sequences for Marvel’s Secret Invasion show. The result was one of the first large-scale use of AI content in a Hollywood production. Such AI-produced images are, admittedly, still rough around the edges. But, nonetheless, this first wave of AI-created images gives a taste of what could be coming in the next decade of AI tools.

Where Render Farms Step In?

Training new AI models and using AI to create long animations takes an immense amount of power and data processing. Large data models, compute power, and extensive neural networks are what makes AI possible, and even the latest generation of consumer-grade hardware lacks the features and compute power to perform most AI tasks quickly. Given this, server and render farms will become increasingly important for making high-quality images in the age of AI. Render farms, though historically used for other tasks, offer all of the hardware needed for AI tasks, making them the perfect platform for generating AI content. Cloud render farms are already working on pivoting to suit the needs of artists using AI, and the options available for artists using AI are likely to only increase from here.

Today, render farms can be used to render projects created with the aid of AI, but in the future, artists will be looking for more power and flexibility as machine learning tools become a part of workflows. As things stand, technically-minded artists can use cloud server rentals, where the user configures the server precisely to their needs, for working with AI models. However, creatively-focused artists aren’t interested in the hassle of configuring a server themselves, and will be looking for easier options as AI tools develop. So, render farm providers will need to pivot to providing easy to use, next-generation rendering tools for artists. It’s still yet to be seen what AI tools will flourish over the next decade; but no matter what tools become part of artists’ workflows, having ready-to-use AI render farms will be vital.

Ethical Considerations

Apart from the clear technical challenges of AI, ethical concerns have led to an industry-wide rethink of how and when AI or machine learning models should be used in artistic projects. Marvel And Method Studios recently faced ample public backlash for how they used AI to create the title sequences for their show, Secret Invasion. The backlash stemmed from concerns that AI is being used to replace artists. Further, many AI models are being trained using content created by artists, bringing up concerns about copyright and compensation for artists.

It’s clear that ethical guidelines must be established before AI can safely and successfully be integrated into the pipeline for artists and studios. Frameworks for such guidelines are already being hashed out by the industry and within legal proceedings, such as the ongoing lawsuit brought against GitHub for its AI-driven Copilot software. Through such proceedings, there’s optimism that ethical rules can be established to allow artists to use AI without worrying about how their job or creative works will be affected. In the right hands, AI can be a powerful tool for creativity for all kinds of artists and creators.

Conclusion

Machine learning and AI are changing the world of computer graphics, from creative inspiration to managing massive data and rendering tasks. As AI tools develop, having the infrastructure and technology of existing cloud render farm services will be vital in making AI accessible to independent artists and studios alike. AI tools will face many challenges in the coming years, but with ethical frameworks and responsible use, AI can be an incredible tool for artists to generate inspiration and make complicated tasks easier.