The field of 3D modeling is changing rapidly with the introduction of artificial intelligence. However, the most important change is not simply that AI can generate 3D models faster. The real transformation lies in how AI is beginning to behave more like a collaborator rather than just a tool. Earlier systems could create shapes from text or images, but newer systems are capable of understanding intent, making decisions, and improving their own outputs. This shift is often described as the move from generative AI to agentic AI.
To understand why this matters, it is important to consider the nature of 3D modeling itself. A 3D model is not just a visual object. It must have proper structure, proportions, and logic so that it can be edited, reused, or even manufactured. A model that looks realistic is not necessarily useful if it cannot function in a design or engineering context. For this reason, recent research is focusing on structured and parametric modeling, where AI generates models that follow rules and can be modified easily (CVPR, 2025).
One of the key developments in this area is that modern AI systems are no longer working alone. Instead of a single model producing a final result, multiple components now work together. For example, one part of the system may interpret a text description, another may generate the geometry, while others check whether the model is physically correct or visually consistent. Systems such as Scenethesis and ShapeCraft demonstrate this approach by breaking down tasks into smaller steps and refining the model through several stages (arXiv, 2025). This process is similar to how a human designer works, gradually improving a model rather than creating it perfectly in one attempt.
This is where the idea of agentic AI becomes important. Agentic systems do not just generate outputs; they plan actions, use tools, and revise their work. In practice, this means AI can assist in real workflows. For instance, Autodesk has introduced AI systems that can interpret a user’s instruction and directly perform actions within software like AutoCAD. Instead of manually executing each step, the user can guide the process while the AI handles the technical operations. This shows that AI is becoming part of the design process itself, not just an external generator.
Another important development is the effort to create shared systems for handling 3D data. Technologies such as OpenUSD are being used as common frameworks that allow different tools and AI systems to work together. This is important because agentic AI becomes much more useful when it can move between platforms, access data, and modify models without restrictions. It allows for a more connected and efficient workflow across design, animation, and simulation.
Real world examples further illustrate how these changes are taking place. Autodesk’s Project Bernini focuses on generating functional 3D objects rather than just visually appealing ones. For example, a generated object such as a container should not only look correct but also be physically usable. Similarly, NVIDIA is developing systems within its Omniverse platform that combine 3D modeling with simulation. These systems can assign materials, physical properties, and behaviors to objects, reducing tasks that previously required many hours of manual work.
Despite these advancements, it is important to recognize the limitations of current AI systems. They can still struggle with maintaining consistency, handling complex details, or ensuring full accuracy in professional contexts. As a result, human oversight remains essential. Designers must review, refine, and guide AI outputs to ensure that the final models meet practical and creative requirements.
In conclusion, the role of AI in 3D modeling is evolving from simple generation to intelligent collaboration. The introduction of agentic AI represents a major step forward, as it allows systems to participate in the reasoning and decision making process behind modeling. This does not eliminate the need for human designers. Instead, it changes their role, placing greater emphasis on creativity, critical thinking, and direction. The future of 3D modeling will likely depend on how effectively this partnership between human expertise and artificial intelligence is developed.
Sources
CAD Llama, CVPR 2025.
Text to CadQuery, arXiv 2025.
Scenethesis, arXiv 2025.
ShapeCraft, NeurIPS 2025 poster.
Autodesk Project Bernini.
Autodesk Wonder 3D in Flow Studio, March 2026.
Autodesk Assistant and AutoCAD agentic workflow material.
OpenUSD documentation and AOUSD Core Specification 1.0.
NVIDIA Omniverse and generative physical AI announcements.
S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
