u/3dskilled

Hitem 3D 2.1v feels like one of the first 3D AI workflows actually oriented toward 3D printing, not just nice-looking preview meshes.

With the new Split to Print feature, the workflow becomes much closer to a real print pipeline:

• Generate a 3D print-ready mesh with Hi3D 2.1v
• Get clean geometry for easier slicing
• Reduce common mesh issues like non-manifold edges, holes, and broken surfaces
• Automatically split the model into printable parts
• Separate complex characters into pieces like head, body, arms, legs, etc.
• Add connectors directly into the mesh for easier assembly
• Automatically arrange the parts for 3D printing
• Download, slice, and print with much less manual cleanup

For characters, figurines, and collectibles, this is the part that actually matters. The hard part is not only generating a good-looking 3D model, but making it printable: cutting the mesh, fixing geometry, adding pins/connectors, and preparing everything for the slicer.

Split to Print makes the workflow much more direct:
image → Hitem 3D 2.1v → Split to Print → connectors → layout → slice → print

NVIDIA just released new research called NeuMatEx, and this one is actually interesting for 3D artists, not just another “nice demo under perfect lab conditions” paper.
The main idea: instead of only extracting standard PBR-style textures from images, NeuMatEx tries to extract neural materials from multi-view captures. These materials can represent more complex real-world surface behavior, like clearcoat, haze, dust, fuzz, scattering, and mixed specular effects, while still being usable for relighting and rendering.

What makes it interesting:

1.Goes beyond standard PBR material extraction

2.Uses multi-view images as input

3.Predicts base color + neural material latents

4.Helps avoid baking lighting and specular artifacts into the texture

5.Targets complex material effects like clearcoat, dust, fuzz and scattering

6.Results are meant to be relightable, not just good from one fixed view

Important detail: this is research, not a simple one-click production tool yet. It is not the same thing as generating a full 3D asset from one image.

But for game dev, VFX, scanning, asset capture, and AI-assisted texturing, this direction feels pretty big. Geometry generation is improving fast, but material capture is still one of the hardest parts of making AI-generated or scanned assets actually usable in real scenes.

Project: https://nvlabs.github.io/neumatex/

it already include multiple game-like systems: leveling, progression, weapon skins, fps mechanics, and pretty smooth real-time 3d graphics.

source: https://x.com/klattkev/status/2064733305698668869

try it here https://xarena.io/

Tractive is an AI-assisted retopology tool designed to make one of the most repetitive parts of the 3D workflow faster: turning messy high-poly or generated meshes into cleaner, more usable topology.

And honestly, retopology is exactly the kind of place where AI makes sense. It is important, technical, and production-critical, but it is also one of the most tedious and least creative parts of asset preparation. A good AI tool here does not need to replace the artist. It just needs to remove the boring cleanup work and help artists get to rigging, animation, and game-ready assets faster.

We already posted about Tractive earlier, but at that time it was mostly an early demo with limited details. Now there is more information available, the tool is being tested, and according to the latest update, it is expected to roll out soon / next month.

Key highlights:

• AI-assisted retopology for cleaner quad-based meshes
• Designed for animation-ready topology, not just visual cleanup
• Useful for rigging, deformation, and character workflows
• Fast annotation-based workflow for drawing topology direction, loops, and boundaries
• Topology recalculation while editing, instead of fully manual rebuilding
• Stitching and region-based control for cleaner mesh flow
• Practical for characters, creatures, props, and game-ready 3D assets
• Potentially very useful for AI-generated 3D models, where topology is often the weakest part of the pipeline

This is one of the more practical AI use cases in 3D. Not “press one button and replace the artist,” but a tool that can fit directly into real production workflows: generate or sculpt a model, clean the topology faster, then move into rigging, deformation, animation, and Unreal / Unity-ready assets.

If AI-generated 3D is going to become truly usable in production, tools like this will be extremely important. Mesh generation is only one part of the pipeline. Clean topology is what makes those assets actually usable.

Sources:
80 Level article
Developer update on LinkedIn

Hyper3D just teased one more feature for Rodin Gen-2.5: a new 12K Texture Generator
The idea is pretty simple: push the Texture Thinking Effort setting to Extreme-High, and the model generates much sharper texture detail, even when zooming in close.
Key points:
12K texture generation
Designed for high-detail 3D assets
Better close-up quality for faces, skin, props, and organic surfaces
Works through the new Extreme-High texture effort mode
Currently available in beta for business subscribers
This could be especially useful for cinematic assets, close-up renders, character work, and any workflow where normal AI-generated textures start falling apart under zoom.

I’m currently making a simple solo game, and I’m not really a 3D expert, so I’ve been using AI tools to speed up parts of the workflow.

For this vehicle, I started with my own references, ideas, and rough concepts, then refined them with ChatGPT. I also tested Meshy during the process, but lately the quality has felt really poor and inconsistent for my use case, so for this project I preferred the results from Hitem 3D and continued with Hi3D for the asset generation.

Instead of generating the whole vehicle as one piece, I broke it down into separate parts like the body, wheels, and other components. That made the process much easier to control, clean up, and assemble later.

Pipeline:

* own references, ideas, and rough concepts
* concept refinement with ChatGPT
* tested Meshy, but the quality was not good enough for this use case
* 3D generation in Hitem 3D
* separate generation of parts like the body, wheels, and details
* assembly and cleanup in Blender
* texture refinement in Substance Painter
* texture baking in Marmoset Toolbag 4
* final implementation in Unreal Engine 5.5.4

The final result is a playable vehicle integrated into UE 5.5.4 as part of my solo game workflow.

I tested the retopology in the new Rodin Gen 2.5, and honestly, I was surprised by the result.

The original mesh had almost 2 million polygons and pretty messy topology. Rodin managed to optimize it down to around 3,000 faces while keeping the overall shape readable and producing a much cleaner quad-based mesh.

It is obviously not a perfect final production mesh, but as a starting point, this is actually very useful. You can treat it almost like a clean base mesh or blockout and continue modeling, refining, baking, or rebuilding details from there.

The PBR textures also came out pretty decent, which makes the result feel more complete instead of just being a simplified mesh with no usable material information.

Stats:

• Input: almost 2M polygons
• Output: around 3K faces
• Cleaner quad-based topology
• Decent PBR textures
• Useful as a base mesh / starting point for further work

Vibe-coded another game selection UI and used AI-generated 3D assets for the characters

Workflow:

• UI concept generated with GPT Images 2
• UI refined / iterated with Gemini 3.1 Pro
• 3D assets generated from GPT Images 2 concepts
• Final 3D models made with Tripo AI

You can compare Tripo AI with other major 3D AI generators side-by-side here Top3D.ai 3D AI ARENA

Rodin Gen-2.5 in low-poly mode and honestly didn’t expect the topology to look this clean.

This started from just one image, and the result came out as a low-poly mesh with surprisingly solid structure. The main forms are kept well, but the biggest thing for me is that the topology doesn’t look random or messy.

It still needs inspection and cleanup, obviously, but as a fast low-poly base mesh, this looks very promising.