From 10-Million-Face Meshes to Part Separation: Hyper3D Moves AI Into Print Preparation

AI-generated 3D models have come a long way, but creating a model is only the beginning of the 3D printing process. A digital asset still needs to be edited, repaired, prepared for fabrication and, in many cases, divided into multiple printable components before it can become a physical object.

Hyper3D, a generative 3D platform developed by Deemos, used Formnext Asia Shenzhen to argue that AI should handle those steps too. Around its Rodin Gen-2.5 generation model, the company showed tools that rebuild a messy mesh, edit specific regions of a finished model with a prompt, split a complex object into parts that fit on a print bed, and assign colors for multi-color 3D printing workflows.

Hyper3D’s stand at Formnext Asia Shenzhen 2026. Photo Credit: Hyper3D

From AI generation to high-resolution geometry

A 3D model that looks realistic on screen does not necessarily contain enough geometry to reproduce the same level of detail as a physical object. Textures and normal maps can make bumps and grooves look three-dimensional even when those details are not actually part of the mesh. A printer has only the geometry to work with.