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The whole truth behind 3D Gen AI models

GenAI helps creators streamline their workflow when creating 3D assets by harnessing the power of artificial intelligence and generative algorithms. But, it is not without its shortcomings. Read below to see its current applications, limitations, and future possibilities.

Sample prompt bar for an AI software
Prompt-based 3D asset generation will gain traction in 2024.

Picture a world where you can collaborate with an algorithm to write a novel, where neural networks create breathtaking 3D models, or where your virtual assistant not only answers your questions but also spins captivating stories or designs unique experiences just for you. That's the amazing power of generative AI.

With a projected valuation of approximately 45 billion U.S. dollars by the end of 2023, the generative AI market is poised for exponential growth from 2023 to 2030 by almost doubling in size. Even the McKinsey group has indicated that the value of gen AI applications will add up to $4.4 trillion to the world economy in a year. Meanwhile, the 3D mapping and 3D modeling market size stands at USD 7.48 billion this year and is expected to reach USD 14.82 billion by 2029, growing at a CAGR of 14.67% during the forecast period.

Think for a moment about combining some of these valuations for the 3D GenAI industry. That's a lot of money. Right?

Therefore, let’s begin with GenAI and its impact on everyday moments to substantiate these valuation claims.

What is GenAI, and how does it work?

In layman's terms, Chatgpt, Luma, etc, are what we would call models of GenAI. Wikipedia would describe it as ”AI capable of generating text, images, videos, or other data using generative models, often in response to prompts.”

What is 3D gen AI, and what industries is it prominent in?
Revolutionizing writing with 3D Generative AI

For example, in entertainment, GenAI personalizes content creation through algorithms that analyze user preferences, like Netflix's recommendation algorithm for movies/TV shows to watch based on watch history. In healthcare, it accelerates diagnostics by analyzing medical images and patient data, and financial services benefit from GenAI's ability to detect patterns indicative of fraud. 

It does amazing things, to be sure. But to understand how it functions, let us guide you through its process. 

  1. Data Collection: First, the GenAI needs a lot of examples from which to learn. For text generation, this might be books, articles, or websites. For images, it could be thousands of photos.

  1. Training: Then GenAI looks at all these examples and learns patterns. For text, it learns how sentences are structured and which words usually come together. For images, it learns about shapes, colors, and how objects look.

  1. Neural Networks: Layers of Neurons: Think of a neural network as a big web of connected points. Each point, or "neuron," processes part of the information. The network has multiple layers, each helping refine the understanding of the input data.

  1. Activation Functions: These help the network make decisions by introducing complexity and generating new content. It's like teaching the network to recognize more subtle details.
  • Text Generation: When you give a text-generating AI a starting sentence, it predicts the next word based on what it has learned. It continues this process to create a full sentence or paragraph.
  • Image Generation: Techniques like GANs (Generative Adversarial Networks) are used to generate images. A GAN has two parts:
    • Generator: Tries to create realistic images.
    • Discriminator: Judges whether the images are real or fake, helping the generator improve.

  1. Improvement and Fine-Tuning
  • Feedback Loop: During training, the GenAI gets feedback on how close its outputs are to the real examples. This helps it improve over time.`
  • Fine-Tuning: Sometimes, a pre-trained model (already learned from a huge amount of data) is fine-tuned on specific data to improve performance for a particular task.

  1. Using the AI-Inference: Once trained, the GenAI can generate new content. You might input a topic or a few words for text, and the AI writes an article or a story. You might describe a scene for images, and the AI draws it.

Now, the question arises: “How does GenAI work with the 3D industry?”

How does Gen AI work on 3D models?

A quintessential GenAI software for 3D spews out data after being given prompts and is trained on millions of 3D models. Before getting into its technical workings, let’s understand how it works in text-to-3D and Image-to-3D.

  1. Text-to-3D - Techniques such as NVIDIA's Magic3D are paving the way for crafting high-quality 3D models from detailed text descriptions. These models can reflect complex shapes and intricate details based purely on descriptive text, making the process accessible even for those without traditional 3D modeling skills. However, the field is still in its nascent phase, with ongoing research needed to improve accuracy and versatility.
  2. Image-to-3D - Tools like Alpha3D can create basic 3D models from single images. These models often serve as a solid starting point but may require further refinement to capture more complex details and nuances. Current technologies can handle simple objects well, but they struggle with intricate or highly detailed subjects, indicating the need for continued advancements to enhance precision and depth.
An immersive 3D shoe model showcasing realism and clarity
Innovative 3D shoe Model Created Using Generative AI

Now, the question is how are these models trained to do that? And do all the other platforms that create 3D models work the same way? The short answer is somewhat, which is why we have investigated the workings of GenAI 3D tools in depth to obtain this information. Let’s get into it.

1. Understanding 3D Models

  • Voxel Grids: Imagine dividing a 3D space into tiny cubes, similar to constructing a structure with LEGO bricks. These small cubes, called voxels (volumetric pixels), represent a specific point in the 3D space. Like LEGO bricks, these voxels can be stacked, combined, and manipulated to create complex 3D shapes and structures. By adjusting the resolution of the voxel grid, one can achieve varying levels of detail and precision.
  • Point Clouds: Envision a cloud consisting of countless tiny dots, each with a unique space position. This is a point cloud, a collection of data points defined in a 3D coordinate system. Each point represents a specific location in space, often captured using 3D scanning technologies like LiDAR or photogrammetry. These data points can be processed to create detailed 3D models.
  • Meshes: Think of a mesh as a net woven from points connected by lines, forming a 3D surface. This net, composed of vertices (points), edges (lines), and faces (the enclosed areas), creates the structure of a 3D object. Meshes are fundamental in computer graphics and 3D modeling, providing the framework for rendering detailed and realistic shapes. By manipulating the vertices, one can adjust the shape and complexity of the mesh, allowing for the creation of everything from simple geometric forms to intricate organic structures. Meshes are the backbone of animations, video games, and simulation.
  • Implicit Functions: Use mathematical formulas to define the shape of a 3D object, moving beyond traditional point-by-point modeling. Implicit functions describe surfaces and volumes through equations, creating smooth and continuous shapes that can be easily manipulated and transformed. Implicit functions are powerful tools in fields like computer-aided design (CAD) and computational geometry, enabling the creation of complex and precise models with mathematical elegance. 

2. Preparing and Using Data

  • Preprocessing: Before feeding the data to the AI, 3D models are resized and centered so they all fit into the same space.
  • Data Augmentation: The data is tweaked in various ways (like rotating or scaling) to give the AI more examples to learn from.

3. Types of AI Models

  • 3D Convolutional Neural Networks (3D CNNs): 3D Convolutional Neural Networks operate similarly to traditional 2D CNNs used in image processing but extend their capabilities into three dimensions. Instead of scanning through a flat, two-dimensional image, 3D CNNs analyze volumetric data, which includes an additional depth dimension. This makes them particularly adept at processing 3D models and volumetric data from medical imaging (e.g., MRI and CT scans) and even video sequences where temporal information is considered the third dimension.
  • PointNet and PointNet++: These are special AI models designed to understand 3D shapes made up of points (point clouds). PointNet takes a unique approach by treating each point individually and using a shared multilayer perceptron (MLP) to process them. It then pulls these features together with a max pooling function, which cleverly ensures that the model isn't thrown off by the order of the points. This allows PointNet to effectively capture the overall shape, making it great for tasks like object classification and part segmentation.

PointNet++ takes this a step further, breaking the point cloud into overlapping regions and applying PointNet to each segment, allowing it to capture detailed local features at different scales. This hierarchical method helps PointNet++ handle varying point densities and complex shapes, making it even better at recognizing intricate details. These advancements make PointNet and PointNet++ essential for applications in 3D modeling, where precision is key. 

  • Graph Neural Networks (GNNs): GNNs represent the vertices (points) and edges (connections) of a mesh as a graph, allowing them to capture both local and global geometric relationships. Each node in the graph exchanges information with its neighbors through iterative message-passing steps, updating its state based on the received information. This process enables the network to learn complex dependencies and relationships within the mesh. GNNs can make sophisticated predictions or classifications about the mesh's overall shape or specific regions by aggregating the information from all nodes. This flexibility and rich feature extraction capability make GNNs exceptionally suited for tasks like shape classification, segmentation, and deformation prediction. Their ability to handle irregular and complex mesh structures makes them invaluable in fields such as 3D modeling, where realism and accuracy are paramount.
Diagram of a Generative Adversarial Network (GAN) showing two neural networks, the generator and the discriminator. The generator creates fake data, while the discriminator evaluates real and fake data, with feedback loops between them to improve accuracy over time.
A Generative Adversarial Network (GAN) illustrating the interaction between the generator and discriminator networks during the training process.
  • Generative Adversarial Networks (GANs): enhance the visual quality of 3D models by synthesizing detailed shapes and textures, making them more lifelike. They can also take low-quality 3D scans and improve their resolution, adding finer details and accuracy. 
    • Generator: This part of the AI tries to create realistic 3D models from scratch.
    • Discriminator: This part tries to tell real 3D models apart from the ones created by the generator. They play a game where the generator gets better at creating realistic models.
A diagram of a Variational Autoencoder (VAE) showing its encoder and decoder structure. The encoder transforms input data into a latent space representation, while the decoder reconstructs the data from this latent space. The diagram highlights the key components, including the input layer, hidden layers, latent space, and output layer.
Exploring the architecture of Variational Autoencoders (VAEs) - a powerful deep learning model used for generating and reconstructing complex data with applications in image synthesis, anomaly detection, and more.
  • Variational Autoencoders (VAEs): These AI models learn to compress 3D models into a simpler form and then expand them back out, which helps them understand and generate new 3D shapes.
    • Auto-Regressive Models: These generate 3D models step-by-step, building the shape piece by piece.
    • Implicit Neural Representations: These use neural networks to directly create smooth and continuous 3D surfaces instead of using fixed shapes like cubes or points.

4. Teaching and Evaluating the AI

  • Loss Functions: Think of loss functions as the AI's grading rubric. Just like a teacher grades a student's work based on predefined criteria, loss functions assess how well the AI creates 3D models. Loss functions quantify the difference between the AI-generated model and the desired outcome. By analyzing these differences, the AI can understand where it needs improvement and adjust its approach accordingly.
  • Optimization: AI uses techniques to improve its performance over time, such as studying for a test.
  • Evaluation: We check the quality of the AI’s work using measures like how closely the generated model matches real ones.

5. Finishing Touches and Applications

  • Refinement: Some touch-ups, like smoothing out rough edges, might be needed after creating a model.
  • Conversion: Sometimes, the 3D model needs to be changed from one form to another, like from a point cloud to a mesh.

6. Applications:

  • 3D Object Generation: Automatically making new 3D models for various digital applications. 
  • 3D Reconstruction: Building 3D models from incomplete data, like creating a full object from a single photo.
  • Shape Completion: Filling in missing parts of a 3D object.
  • Customization: Allowing users to create personalized 3D models.

Generative AI for 3D models is like having a smart machine to create or modify 3D shapes, such as those used in video games, movies, and virtual reality.

Top GenAI 3D models in 2024

The 3D Generative AI scene has been pretty exciting lately, as we've seen some up-and-coming developments in the industry. As we dive into the top 3D Generative AI models of 2024, it's amazing to see the innovation and creativity these models bring to the table. Therefore, let's look at which GenAI has been making waves in 3D modeling.

GenAI for 3D models such as Luma AI, Stable projectors,, Meshy, Latte3D
Which are the best GenAI models for 3D in 2024


Luma AI is a generative AI 3D software company that leverages advanced neural networks and computer vision techniques to create 3D models from text, images, and videos. The company's primary product, Genie, allows users to generate 3D objects quickly by typing descriptive text. Based on the description, Genie produces four distinct models, which can be customized and viewed in augmented reality. These models can be exported to various platforms, including art software like Blender and game engines like Unreal and Unity.

Additionally, with its capture feature, users can simply take images or videos of an object from various angles from their phones. The AI-powered algorithms stitch these visuals together, creating a highly accurate 3D representation. This seamless process eliminates the need for expensive and complex 3D scanning equipment, making high-quality 3D modeling accessible to everyone, from hobbyists to professionals.

Luma AI employs Neural Radiation Fields (NeRF) to reconstruct 3D scenes from 2D images. This approach enables the creation of high-quality 3D models for video games, virtual reality, and other applications requiring 3D content. The company aims to democratize 3D content creation, making it accessible even to those without specialized skills in 3D modeling.

Strengths of Luma AI's Generative AI 3D Software:

  • User-Friendly Interface: Genie allows easy 3D model generation from simple text prompts, making it accessible to non-experts.
  • Rapid Prototyping: The software quickly generates multiple 3D model variations, facilitating fast iteration.
  • High-Quality Outputs: Utilizes Neural Radiance Fields (NeRF) for detailed and accurate 3D models from 2D images.
  • Compatibility: Models can be exported to platforms like Blender, Unreal Engine, and Unity.
  • Continuous Improvement: Luma AI actively gathers feedback and updates the software with new features.
  • Strong Backing: Supported by significant funding and partnerships with industry experts.

Weaknesses of Luma AI's Generative AI 3D Software:

  • Imperfect Outputs: Early-stage development may lead to inconsistent model quality.
  • Cost Considerations: Some features or high-quality outputs might require payment.
  • Accuracy of AI Interpretations: The AI may not always perfectly capture the user's vision from textual descriptions.

The core of’s technology is its text-to-3D generation capability, known as 3DFY Prompt. Users can input descriptive text, and the software generates detailed 3D models that are semantically meaningful and of high quality, including well-formed mesh topology, UV coordinates, and PBR textures. This is particularly useful for creating large datasets of 3D items or virtual objects for various applications such as gaming, AR/VR, simulation, and online retail​.

Strengths of

  • Ease of Use: Generates 3D models from simple text prompts, accessible to non-experts.
  • High-Quality Outputs: Produces detailed models with excellent mesh topology and textures.
  • Scalability: Automates the creation of numerous 3D models and offers API integration.

Weaknesses of

  • Category Limitations: Generates models only in specific developed categories.
  • Functional but not Creative: It is functionally focused instead of creatively focused and generates 3D models without any creative flair.
  • Early-Stage Features: Some features are still in the alpha stage and potentially unreliable.

Discover the power of Generative AI in 3D model creation! This technology automates the design of characters, architectural structures, and complex product prototypes, and drastically helps in reducing production time and enhancing creative possibilities. See which applications are leveraging 3D Gen AI to push the boundaries of what's possible in 3D modeling.


Latte3D is a generative AI model developed by NVIDIA that allows for the real-time creation of high-quality 3D models from text prompts. The model operates in two stages. Initially, it uses volumetric rendering to train the objects' texture and geometry. In the second stage, the textures are refined using surface-based rendering techniques, enhancing the visual quality of the generated assets. This two-stage process ensures that the final output is both detailed and accurate.

Strengths of Latte3D:

  • Generates high-quality 3D models from text in near-real-time.
  • Offers multiple shape variations for each text prompt.
  • Easily integrates with other graphics software, supporting seamless workflows.
  • Enhanced text understanding through diverse prompt training.

Weaknesses of Latte3D:

  • The current output quality is lower than that of manual artist-created models.
  • Limited to initial training datasets of animals and everyday objects.
  • Difficult to achieve highly specific customizations.
  • Still a proof of concept, not publicly available yet.

Stable Projectorz

Stable Projectorz is an innovative AI-driven 3D texturing tool that leverages the capabilities of the Automatic Stable Diffusion model. This free software is designed to assist artists and game developers in creating high-quality textures for 3D models efficiently on their computers without needing expensive hardware or subscriptions.

Strengths of Stable Projectorz:

  • Free to use, making it accessible to hobbyists and small studios.
  • User-friendly interface and straightforward installation process.
  • Generates high-quality textures that preserve original UV mappings.
  • Allows the creation of multiple art variants, the blending of textures, and fine-tuning of adjustments like hue and contrast.
  • Includes features like ambient occlusion shading and depth control nets for enhanced realism and accuracy.
  • Open-source project with active community support and collaboration.

Weaknesses of Stable Projectorz:

  • Depends heavily on the user’s hardware; it is slower on less powerful systems.
  • It lacks an undo/redo feature and requires frequent saves to avoid losing work.
  • Limited 3D viewport controls and export options compared to commercial software.
  • The free version adds a watermark to generated textures, limiting commercial use.
  • Advanced features like control nets and inpaint masking have a steeper learning curve.
  • Ongoing development can result in occasional bugs and stability issues.


Meshy is an AI-powered tool designed to revolutionize 3D content creation. It allows users to generate detailed 3D models and textures from text descriptions and images. This could be a significant advantage for content creators, game developers, and digital artists, as generating 3D environments and objects, along with concept art and prototyping, could become much faster and more efficient.

Strengths of Meshy:

  • Speed and Efficiency: Meshy promises to create 3D models and textures in minutes, potentially saving users a lot of time.
  • Accessibility: With an intuitive interface and AI capabilities, 3D creation could become more accessible to a wider range of users, not just experienced 3D modelers.
  • Quality Output: Based on reviews, Meshy seems to generate decent-quality 3D models.

Weaknesses of Meshy:

  • Limited Creativity: While Meshy can create models based on descriptions, it lacks the flexibility for highly creative or unique designs that require a human touch.
  • New Technology: As a new AI tool, Meshy might have limitations or require further development.
  • Cost: Meshy's pricing is currently not publicly available, so it's difficult to say if it would be affordable for all users.
  • Potential for Errors: There's always a chance of errors in the generated models, such as inaccuracies in shape, texture, or overall design.

After analyzing the significance and weaknesses of these tools in depth, let’s consider how they affect 3D artists.

A friend or a foe for 3D artists?

Whether AI is perceived as a friend or a foe depends on how it's integrated into the artistic process and the mindset of the individual artist. But let’s begin by explaining why and how GenAI is a significant ally of 3D artists. 

Speed and Efficiency: Tools like Adobe's Firefly in Substance 3D streamline the creation of textures and materials from simple text prompts. This reduces the time needed for base design creation and prototyping, allowing artists to focus more on the creative aspects of their work.

Enhanced Creativity: AI-driven features such as Adobe's Substance 3D Text-to-Texture and Generative Background empower artists to experiment with unique designs and environments that might not have been feasible manually. These tools provide fresh perspectives and open up new avenues for creative exploration.

On the contrary, one cannot refute these points on how genAI is making things hard for 3D artists who are using it as a tool for their work - 

  1. Job Displacement: There's a concern that AI automation may lead to the displacement of specific jobs or tasks traditionally performed by 3D artists, particularly those involving repetitive or less creative work.
  2. Dependency and Skill Erosion: Over-reliance on AI tools could lead to a decline in specific skills among artists, as they may become accustomed to relying on automated solutions rather than developing their abilities.
  3. Ethical Concerns: The use of AI in creating art raises ethical questions regarding authorship, ownership, and authenticity. Artists should consider these implications when incorporating AI into their workflows.

Therefore, AI, when used within limits and as a tool rather than a replacement, can aid artists in some mundane tasks.

Current Limitations of 3D Gen AI Models

Generative AI has made significant strides in various domains, including 3D modeling, but it still grapples with several limitations that hinder its widespread adoption and effectiveness.

An illustration depicting the challenges faced by generative AI in creating complex 3D models, highlighting issues like quality, detail, and real-time rendering.
Exploring the Boundaries: Understanding the Limitations of Generative AI in 3D Modeling
  1. Current limitations 
  • Quality Control: Maintaining consistent quality across generated 3D models remains a challenge. While GenAI can produce many models, ensuring each meets specific quality standards and functional requirements is difficult. Human intervention is often necessary to refine and validate outputs.
  • Visual Quality: 3D AI tools often struggle to produce visually appealing outputs. Generated models may lack detail, coherence, or realistic features, resulting in blob-like meshes unsuitable for many applications beyond basic prototyping or background elements.

  • Intellectual Property and Ethics: Generative AI raises complex ethical and legal concerns about intellectual property rights, data ownership, and algorithmic accountability. Determining liability for errors or misuse of generated content can be challenging, especially when decisions lack human oversight or involve sensitive intellectual property.

  1. Development limitations
  • Data Dependency: Generative AI models typically require large datasets for training, which can be costly and time-consuming to curate. The quality and diversity of training data directly influence the model's performance and generalizability, posing a barrier to entry for organizations with limited access to relevant data.

  • Cost of Cloud Hosting: Hosting generative AI models on cloud platforms can incur significant expenses, particularly for large-scale projects or those requiring frequent model iterations. High hosting costs deter smaller businesses or individuals from leveraging advanced AI capabilities.

  • Computational Demands: Training and running generative AI models demand substantial computational power, often exceeding the capabilities of standard hardware. This reliance on powerful hardware increases operational costs and limits accessibility for users without access to specialized computing resources.

  • Performance and Stability: Despite advancements, generative AI models, particularly those based on Generative Adversarial Networks (GANs), are susceptible to issues such as mode collapse. Mode collapse occurs when the generator produces a limited variety of outputs, diminishing the model's overall performance and stability.

Ongoing research and development efforts are addressing these limitations to enhance generative AI technologies' robustness, efficiency, and ethical framework. Collaborative initiatives involving researchers, industry stakeholders, and policymakers are essential to navigate these challenges and realize the full potential of AI-driven 3D modeling.

Why can’t 3D GenAI not take over the 3D industry for now?

Even though AI has been making big waves in many areas, it's not ready to take the reins in the 3D industry just yet. It's missing that essential human touch.

  1. Small Training Sets: Think about Autodesk’s Bernini. It's been trained on 10 million diverse 3D shapes. Now, that might sound like a lot, but it's tiny when you compare it to something like GPT-4, which was trained on around 10 trillion words. For 3D AI to get really good, it needs to learn from more examples, and gathering all that data takes time.
Display of a 3D model with intricate geometric patterns, showcasing the training process of a 3D Generative AI. The image highlights various stages of model development, including wireframe views and texture application.
3D Generative AI Model Training: Visualizing Complex Patterns
  1. Limited Knowledge: Most of these AI models have a pretty narrow focus. For example, Latte 3D knows a lot about animals and everyday objects, while Alpha 3D is great with shoes and furniture. However, this limited training means they can't create a wide variety of 3D models yet.

  1. Existing Libraries: There are already some fantastic 3D asset libraries, like The Base Mesh, which offer ready-to-use 3D models created by artists that are often free or very affordable. These assets are usually polished and ready to go, unlike many AI-generated models that need much tweaking before they’re usable.

  1. Experimental Stage: Many 3D Generative AI tools are still experimental and are only available to the public through their research projects. Additionally, they’re not ready to deliver consistent, high-quality results across various categories, and their output is limited because of severe development limitations. 

In short, while 3D Generative AI has much potential, it's not quite ready to take over the 3D modeling world. It needs more data, a broader learning base, and a lot more real-world testing before it can become a game-changer because as the 3D world is all about collaboration and client needs, human artists shine because they can communicate and understand what people want. AI just can't replicate that level of intuition and connection.

Sure, AI can handle some of the grunt work in 3D modeling and animation, but humans still need to add that special flair and personality. After all, our unique touch gives 3D creations that wow factor. 

Some companies claim to generate high-quality 3D models using their in-house GenAI, but it is speculated that a 3D artist is actually making the model from start to finish, and the asset is delivered under the false pretense of being created through GenAI. 

Are people currently using GenAI for 3D modeling?

Yes! GenAI is increasingly utilized for various aspects of 3D modeling and related tasks. By leveraging advanced machine learning algorithms, GenAI enables the rapid creation and optimization of 3D models, saving time and effort for artists and designers. This technology is being integrated into tools and platforms across multiple industries while still relying on the essential input of human creativity and expertise.

Here's how it's being applied:

A diverse group of people using Generative AI for various tasks, including 3D model creation, music composition, coding assistance, and interactive chat applications.
From creating 3D models and generating human-like conversations, people are leveraging Generative AI in innovative ways to enhance creativity, productivity, and problem-solving.
  • Base Meshes: GenAI can generate base meshes for 3D models, providing a starting point for artists to build upon. These meshes can be adjusted and refined according to the project's specific needs.

Tools currently using GenAI to create base meshes- NVIDIA Omniverse with GANverse3D, Lumirthmic

  • Background Assets: GenAI can assist in generating background assets such as landscapes, buildings, foliage, and other elements that populate a scene. This can save time for artists who need to create complex environments.

Tools currently using GenAI to create background assets–

  • Assets for Mobile Games: GenAI can help generate assets optimized for use in mobile games, where performance and file size are crucial considerations. These assets can include characters, props, and environments tailored to the requirements of mobile platforms.

Tools currently using GenAI to create mobile games assets - Luma AI, Masterpiece Studio, Avaturn, and more

  • Optimizing and Assisting 3D Artists: GenAI can assist 3D artists by automating repetitive tasks, optimizing models for performance or rendering, and providing suggestions for improving workflows or designs.

Overall, using GenAI in 3D modeling offers opportunities to streamline the creative process, enhance productivity, and explore new design possibilities. However, while AI can assist in various aspects of 3D modeling, human creativity and expertise remain essential for achieving high-quality results.

What the future holds for GenAI in the 3D industry 

Generative AI is poised to revolutionize the world of 3D modeling, but it's still early days. Technological advancements, including improved algorithms and hardware developments, coupled with broad industry adoption and collaborative ecosystems, are set to drive significant growth in this space. Key players such as NVIDIA, Google, and Autodesk, alongside innovative startups like Luma AI, are poised to lead the way in integrating AI capabilities into 3D workflows, ultimately shaping the future landscape of AI-driven 3D content creation and innovation. Here's a glimpse into what the future holds based on current trends and research:

  • AI-powered Design Assistants: Tedious tasks like retopology (optimizing polygon structure) and UV unwrapping (preparing textures) could become a thing of the past. GenAI assistants could handle these tasks in the background, freeing up 3D artists for more creative endeavours.  Research by companies like Autodesk is exploring how AI can analyze data and generate multiple design options, accelerating the design process.

  • Enhanced Realism and Automation: Imagine AI that can automatically generate realistic textures, lighting, and materials for your 3D models. This is another area where GenAI is making strides. Companies like Meshy are developing AI tools that can analyze real-world materials and generate digital representations for 3D models, enhancing their realism.
A 3D artist's opinion on the future of 3D Gen AI:
I'm an indie developer looking for anything to help speed up my workflow. I use AI for many things, but 3D models are years from worthwhile. For main characters and important high visibility assets, still, fully contract out things or heavily remaster/reauthor existing stuff.


While 3D generative AI shows immense promise, its journey is still unfolding. It could become as transformative as ChatGPT with careful nurturing and innovation, reshaping the 3D modeling and design landscape. Companies developing GenAI for 3D models face issues like ensuring consistent quality, achieving visual realism, and acquiring sufficient training data.

Additionally, cost and accessibility limitations can hinder widespread adoption. Despite these hurdles, advancements in AI-powered design assistance, realistic material generation, and text/image-to-3D conversion are on the horizon. GenAI is poised to become a valuable tool, but it will likely work alongside human creativity, not replace it.


What is the importance of good quality data in training Generative AI models?

Generative AI models learn from extensive, high-quality datasets to generate diverse, creative outputs. Clean and accurate data is crucial for optimal performance, while poor data quality can lead to models generating nonsensical, biased, or low-quality outputs.

Can Generative AI be used for real-time applications?

Yes, with advances in hardware and optimization techniques, Generative AI can be used in real-time applications like interactive chatbots, live video effects, and real-time music generation.

What datasets are used to train Generative AI 3D models?

Commonly used datasets include ShapeNet, which offers over 3 million CAD models across 3,135 categories, and ModelNet, which contains 3D CAD models categorized into 40 and 10 classes for various shape classification tasks. Additionally, some companies like Autodesk have their own databases, such as Make-a-shape. 

How does Generative AI differ from AI?

Generative AI specifically creates new data, such as images, text, or music, that mimic the characteristics of the training data. Other types of AI, often referred to as discriminative AI, focus on classifying or predicting outcomes based on input data.

Can you give an example to illustrate the difference between Generative AI and discriminative AI?

Generative AI can generate entirely new images of cats and dogs that do not exist in the training data.

Discriminative AI can look at an image and determine whether it is a cat or a dog based on the features it has learned from the training data.

Nishant Verma profile image Nishant Verma
Nishant is the VP of 3D design at Ikarus 3D and specializes in VR, AR, and all things 3D. He is passionate about pushing boundaries and fostering collaboration through information in the industry.