The current state of generative AI in 3D

Generative 3D is revolutionizing how we approach digital design! Like the generative art tools we’ve all become very familiar with, it relies on algorithms and AI to handle much of the creative process, in this case, automatically producing 3D models, environments, and objects. But to what degree has AI penetrated the workflow of 3D professionals, and what’s possible nowadays?

Let’s start by clarifying some things. The field of digital design is very much changing due to generative AI 3D. It’s capable of producing 3D models, objects, and surroundings autonomously with the use of algorithms and AI.

Rather than creating every piece by hand, 3D artists enter a few simple parameters, and the program does the rest, frequently producing complex designs and surprising results. With the help of this technology, designers are free from routine tasks and chores.

Curious how this shift compares to other design evolutions? Check out this deep dive into 3D vs flat logo design to see how dimensionality changes impact branding, too.

Here’s why generative 3D stands out:

With advancements in technology that span several decades, generative design has a rich history. Let’s look at how generative design has evolved over the years.

Initial efforts towards generative design were made in the late 20th century. Early adopters, mostly engineers and architects, started experimenting with algorithmic procedures to automate workflow components in design. These early systems followed specific rules or constraints to produce simple design variations, saving time on repetitive tasks.

The 1990s saw procedural generation, a precursor to modern generative design, emerge in fields like gaming and animation. This method allowed developers to create vast, complex environments using mathematical algorithms rather than manual design. Games like SimCity and The Elder Scrolls: Daggerfall were early examples of procedural generation in action, setting the stage for more advanced generative techniques.

Machine learning gained more popularity in the 2000s, enabling computers to learn from data in addition to following rules. This shifted generative design toward more adaptive, intelligent systems that could refine designs based on performance criteria, user preferences, or other data inputs. This was especially useful in industries like automotive and aerospace, where optimizing designs for cost, function, and material efficiency was essential.

The full potential of generative 3D started to show as AI developed. This era saw the rise of advanced tools like Autodesk’s generative design platform and Houdini’s procedural modeling, which gave designers unprecedented control and creativity while automating labor-intensive tasks.

Deep learning and artificial intelligence have been key factors in the breakthroughs made in generative 3D in recent years. These days, AI algorithms can take essential inputs like written descriptions or rudimentary sketches and turn them into incredibly realistic 3D assets.

Innovations like real-time rendering, cloud-based generative tools, and text-to-3D technology make it easier for designers and non-experts to create high-quality 3D content at scale, ushering in a new era of AI motion design workflows across creative industries.

Although generative 3D might sound complicated, it’s much easier to understand than you might think. It creates 3D models automatically using intelligent software by following a few basic rules. Let’s look at the process of how this works in more detail.

At the core of generative 3D, you’ll find algorithms. Basically, these are sets of instructions or rules the computer follows. You tell the software what you want (for example, the size or shape of something), and the algorithm will generate a design based on that.

Generative 3D gets even smarter when artificial intelligence (AI) comes into play. While regular algorithms follow strict rules, AI can learn and improve as it works. The more designs it creates and learns from, the better it gets at making new ones. Machine learning (a part of AI) makes the system smarter over time by identifying trends and enhancing designs with less assistance from a person.

Data is required for the system to produce designs. Think of data like the ingredients in a recipe. The software generates designs based on the information you provide (e.g., the materials you wish to use, the style you want, and any restrictions like size or cost). The better the data you provide, the more accurate and creative the designs can be.

One of the most incredible things about generative 3D is that it can create numerous design variations.

You don’t have to settle on the first thing that it produces. You can change the rules or enter new data; the algorithm will create new possibilities. This enables creators to experiment quickly and watch new ideas come to life without doing everything by hand.

If you’re fascinated by how AI is reshaping creative workflows beyond just 3D, take a look at this article on AI video prompts and how they’re revolutionizing motion content generation.

A variety of powerful tools and applications are making generative 3D more accessible than ever before! Here’s a look at some of the most popular options available today.

Autodesk is a major player in generative design, and software like Fusion 360 is widely used in industries such as engineering and product design.

Houdini excels at procedural generation, perfect for creating complex 3D environments and effects.

Blender is a powerful open-source 3D tool, and add-ons like Sverchok and Animation Nodes bring generative design to this platform.

NVIDIA Omniverse allows real-time collaboration on 3D projects and integrates AI to support generative workflows.

Several generative 3D tools are now available that allow users to create 3D models directly from text inputs, making high-quality 3D design more accessible. Here’s a breakdown of the top platforms and their key features:

Meshy is a versatile platform for quick, high-quality 3D asset generation from text or images. It supports a wide range of 3D file formats and provides users with tools to create fully textured models with minimal effort. Meshy also integrates with popular tools like Blender and Unity, making it highly flexible for developers and designers. Key features include:

Masterpiece X focuses on simplifying the creation of realistic 3D models from text prompts. With its user-friendly interface and advanced AI algorithms, this tool allows users to quickly generate 3D models and animations, making it ideal for game designers, animators, and 3D artists. Key features include:

Luma AI leverages cutting-edge NeRF (Neural Radiance Fields) technology to create 3D models from text and videos. It excels at creating realistic, detailed 3D environments and objects, making it a go-to for AR/VR developers and filmmakers looking for photorealistic assets. Key features include:

Sloyd is a powerful tool designed for parametric 3D model generation from text. Its focus is on creating customizable, procedural 3D models, making it ideal for game developers and designers who need easily modifiable assets. Key features include:

NeROIC uses neural rendering to convert text into highly detailed 3D objects, leveraging cutting-edge AI techniques. This platform excels in creating realistic textures and lighting for various industries, including gaming and architecture. Key features include:

Generative 3D has entered many different industries. Let’s examine some of the key areas where it is making an impact.

Generative 3D is frequently used in the movie industry to create realistic environments, special effects, and even entire characters. By using algorithms to generate complex visuals, filmmakers can achieve beautiful, detailed scenes that would be difficult or time-consuming to design by hand.

Example: In Avengers: Endgame, generative design tools were used to create complex battle environments and digitally de-age characters like Captain America. These tools helped render large, intricate scenes more quickly and efficiently.

In gaming, procedural generation (generative 3D) is often used to produce large, dynamic settings, objects, and characters. This technology allows game developers to quickly create original worlds and gameplay experiences with virtually limitless possibilities.

Example: In No Man’s Sky, procedural generation creates an almost infinite number of planets, each with its unique landscape, ecosystem, and creatures. This allows players to explore an ever-expanding universe without the need for manually created content.

The marketing industry is also beginning to notice generative 3D, giving companies new avenues for producing individualized, engaging content. Using AI-driven tools, companies can generate unique 3D assets, animations, and even interactive experiences tailored to different audiences.

Example: In Nutella’s “Nutella Unica” campaign, they used a generative design algorithm to create millions of unique jar labels. Every label had a distinctive pattern of colors, so no two jars were the same. This gave consumers a sense of exclusivity and helped Nutella stand out through innovative design.

This experimental, tactile approach to visual design also shows up in trends like squishy 3D design, where designers combine soft, plump forms with bold textures and materials—a trend closely tied to generative aesthetics.

Generative 3D is advancing slower than other areas (like audio or image generation) because there’s less 3D data available online. This lack of data makes it more difficult for AI to learn and improve in 3D creation.

While generative 3D offers many exciting possibilities, it also comes with a few challenges and limitations that creators should consider.

Well-established 3D asset libraries like TurboSquid, Envato, Sketchfab, and CGTrader offer high-quality, ready-to-use 3D models artists create. Unlike AI-generated models, these models are often polished and require little to no tweaking, which usually needs significant adjustments before they are usable.

Generative 3D can be demanding on hardware. Complex algorithms and large datasets require significant processing power, which can be a barrier for smaller studios or independent creators without access to high-end computers or cloud-based solutions.

Many 3D generative AI tools are still experimental and available mainly through research projects. They aren’t yet capable of producing consistent, high-quality results across different categories, and their output remains limited due to current development constraints.

Generative 3D is an exciting frontier, offering endless possibilities for design, film, gaming, and more! While it faces challenges (such as limited data availability and ethical considerations) it’s clear that software and AI developments are advancing steadily. From automating 3D asset creation for films and games to enabling complex designs for marketing campaigns, generative 3D is set to play a pivotal role in shaping the future of digital content creation.

If you’d like to learn more about generative AI, take a look at what we’re doing right here with our own ImageGen tool, or on the flip side, why not immerse yourself in collage art—how creatives are going back to basics in the age of AI. Lastly, don’t forget to look at Envato’s newly relaunched 3D category, including thousands of brand-new assets for your chosen 3D tool!