r/TopologyAI

Soon available for comparison on our 3D Arena, side-by-side.

Rodin Gen 2.5 just got announced, and the new update looks pretty strong.

Key points they’re showing:

• Up to 10M polygon generation
• 1M-poly output in around 4 seconds
• Different “thinking effort” levels depending on asset complexity
• 3D-native textures with better detail handling
• Batch generation up to 10 results
• More control for converting models into parts

The detail level looks noticeably stronger now, especially for high-frequency surface details, close-up renders, collectibles, and 3D printing-style assets.

Curious to see how it compares directly against Tripo, Meshy, and other 3D AI generators in the same prompts.

The video above is a showcase of a game generated from a single vibe-coding prompt.

Tesana AI recently introduced Muranyi-3, which they describe as an AI model for generating playable 3D games from text prompts. In this example, the prompt : “Make a multiplayer FPS shooter where you fight zombies in waves and improve your weapons.”

What was generated / showcased:

• Playable 3D game prototype
• FPS-style gameplay
• Zombie wave system
• Weapon mechanics / upgrades
• 3D environment
• Characters and enemies
• VFX and visual feedback
• Animations
• Game logic and basic gameplay structure

Muranyi-3 update highlights:

• Improved graphics and VFX
• Better animations
• More advanced game logic
• Fewer common generation bugs
• Faster loading times
• Faster generation speed

Obviously, this is still closer to an AI-generated 80/20 prototype than a polished production game. But as a playable result from one prompt, it’s an interesting direction for rapid prototyping.

Source / showcase:
https://x.com/givros/status/2053187440131572215

Built a fully playable AI-generated character with physics and animation setup in under 3 hours.

Workflow breakdown ⚙️

• Character concept using AI image generation
• 3D character generation from the concept
• Assembly and geometry cleanup in Blender
• Fast manual UV unwrap
• Texture cleanup and artifact fixing
• Material setup for game-ready use
• Free auto-rigging with AccuRig
• Importing the character into Unreal Engine 5
• UE5 animation retargeting
• Adding cloth physics with Chaos Clothing
• Final gameplay test in Unreal Engine 5

The goal was to test how fast an AI-assisted pipeline can turn a generated character into something actually playable, not just a static showcase model.

full guide https://www.youtube.com/watch?v=-GxVR8s_nZY

Pixal3D, a new open-source image-to-3D model from Tencent ARC Lab, has now been added to the 3D AI Arena on Top3d.ai

The model focuses on high-fidelity 3D generation from single images using a pixel-aligned generation approach instead of traditional canonical-space generation.

Pixal3D was recently accepted to SIGGRAPH 2026 and is based on the newer Trellis.2 backbone.

You can now compare Pixal3D side-by-side with other major 3D AI generators across a dataset of 80+ prompts, including:

• organic shapes
• characters
• buildings
• props and everyday objects
• stylized assets
• game-ready asset tests

Current comparison categories include:

• topology
• texture quality
• generation speed
• generation cost
• low-poly results
• retopology
• segmentation

There is also:

• a leaderboard
• AI-assisted comparisons
• user reviews
• side-by-side model previews

Some of the currently available generators include:

Hunyuan 3D, Tripo AI, Rodin, Meshy, Trellis, Hitem3D, Prism, Seed3D, Wonder3D, Tencent Hunyuan, Pixal3D, and more.

Top3d.ai — 3D AI Arena

A stylized vehicle combat game being built in Unreal Engine 5 by an indie developer using AI-generated 3D assets with Rodin gen 2.0 Hyper 3D

The workflow is surprisingly straightforward: rough ideas and scratch designs are first refined into clean references in NanoBanana, then generated into 3D assets using Rodin Gen 2.0, followed by Smart Low Poly mode for AI retopology before moving into Blender and Substance Painter for texture rebaking, cleanup, and final adjustments.

One of the most useful parts of the workflow has been rodin smart low poly mode it actually produces very clean and usable topology, making the assets much easier to work with afterward and significantly reducing the amount of manual cleanup normally needed.

The final project is assembled and polished inside Unreal Engine 5.

Tested by T-Boy on Rodin Gen-2.5 update, using its extreme-high & high modes.
Hyper3D Rodin is the first 3D AI to achieve 10M+ polygons, the micro-level surface details are incredible!

Started by generating the base robot concepts with a free AI 3D generator, then refined the designs, materials, interface elements, and overall visual style using GPT Images 2.0.

The interactive workflow, UI structure, and implementation were built with Hunyuan 3.1 and Gemini 3.1 Pro.

Workflow:
• generate base 3D robot assets
• refine silhouettes + visual consistency
• generate UI panels/screens with GPT Images 2.0
• build interactive menu + inspection system
• connect everything into a functional prototype interface

This is a pretty interesting open-source toolkit for generating interactive 3D environments from a single input image.

Instead of just creating one isolated 3D asset, it builds more of a small world around the image: environment mesh/splat, dynamic object meshes, physics-based interactions, lighting, ambient audio, and object-specific SFX.

What makes it especially interesting is the “interactive scene” angle. The generated environment is not just something to look at, but something you can explore and interact with, which makes it useful for fast prototyping, game concepts, previz, or quickly blocking out environment ideas.

Outputs include:

• .glb / .obj 3D models
• .spz Gaussian splat environment
• ambient audio and SFX
• Hunyuan 3D support with PBR, LowPoly, face count and polygon options
• Unity / Unreal / Godot / Blender-friendly workflow

Obviously, this is not some magical one-click AAA level generator, because the universe still hates convenience. But as an open-source starting point for turning images into interactive 3D worlds, this looks really useful.

GitHub: https://github.com/neilsonnn/image-blaster

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

Pixal3D is a new free image-to-3D AI model from Tencent.

The interesting part is source-image fidelity. It improves on Trellis.2 to better preserve the structure of the original image, so the generated 3D model stays much closer to the input instead of drifting away.

In this video, testing how accurately it follows the reference image.

GitHub:
https://github.com/TencentARC/Pixal3D

Rodin Gen-2.5 is looking really strong as a high-detail sculpt starting point.

What impressed me most is not just the amount of detail, but how useful this can be in a real 3D workflow.

Instead of starting from a blank mesh, you can quickly generate a high-fidelity base, bring it into Blender, ZBrush or any other 3D software, and continue sculpting / refining from there.

The biggest thing here is time saving.
For creatures, characters and complex assets, this can save a huge amount of time because you already get the main silhouette, forms, surface detail and overall direction very quickly.

Then the artist can spend more time improving the asset instead of building everything from zero.

Why this feels useful:

• strong high-detail sculpt base
• good starting point for creature design
• fast way to block out complex forms
• useful for further sculpting and cleanup
• saves a lot of time in the early production stage
• can be pushed further in Blender / ZBrush
• feels more like a workflow accelerator than a “one click final asset” tool

In the video, I’m showing a winged dragon generated with Rodin Gen-2.5 from Hyper3D, and the amount of small surface detail is honestly impressive.

the creator has now made it open source and shared a full tutorial.

Instead of just generating a standalone 3D model, the workflow turns AI-generated biology assets into a small interactive web app with:

• full 3D model viewer
• selectable labels / info panels
• front + back image workflow
• Tripo 3.1 for the 3D asset
• model compression for web loading
• GitHub Pages deployment
• full tutorial + source code available

This is the part that feels really exciting: you no longer need a full team just to turn a visual idea into a decent-looking interactive app or landing page.

Online experience: https://gordensun.github.io/LearningCell/

Github: https://github.com/GordenSun/LearningCell

You can compare Tripo 3.1 with other major AI 3D generators side-by-side here: Top3D.ai

From concept to a playable procedural 3D platformer in Unreal Engine 5 in just 3 days, using 3D AI.

This was a solo project, and I think it’s a pretty strong showcase of how much game development is changing. The entry barrier is getting lower, and now one person can build a playable 3D prototype with custom characters, assets, and level pieces in less than a week.

Workflow:

• Concept ideas and visual direction were created with Nano Banana 2
• 3D assets were generated with Tripo 3.1 v and Tripo P 1.0
• Mesh cleanup, refinement, and manual fixes were done in Blender
• Characters/assets were rigged with AccuRig (Free)
• Final setup, gameplay, level logic, and implementation were done in Unreal Engine 5

It’s still a prototype, but the fact that this kind of playable 3D game can be built by one person in only a few days is honestly insane.

Full Guide Here; https://www.youtube.com/watch?v=T4jV7O8SMGE

Pretty cool example of where 3D AI can go beyond just generating standalone assets.

The creator used AI-generated 3D models and turned them into an interactive science app with a full UI, model viewer, labels, comparison tools, and extra info panels.

From the post:

• 3D models generated with AI Tripo 3.1
• Interactive web app built around them
• UI design made with GPT Images
• Code done with Gemini 3.1 Pro
• Feels more like an educational product than just a 3D asset demo

You can compare Tripo 3.1 with other major AI 3D generators side-by-side here: Top3D.ai

If you want the code for it, you can access it from here

https://github.com/nareshk106/Acqua

Pixal3D is a new Image-to-3D AI model that turns a single image into a high-fidelity 3D asset with detailed geometry and PBR textures.

The interesting part is its pixel-aligned approach. Instead of only loosely using image features, it back-projects them into 3D space, helping the generated model stay much closer to the original input image.

Highlights:

• Single image to 3D asset
• Detailed geometry
• PBR texture output
• GLB export
• Code, model, paper and demo released
• Built around better pixel-to-3D correspondence

This feels closer to reconstruction-level fidelity, while still keeping the creative flexibility of 3D generation.

Project page:
https://ldyang694.github.io/projects/pixal3d/

GitHub:
https://github.com/TencentARC/Pixal3D

Huggingface Demo:
https://huggingface.co/spaces/TencentARC/Pixal3D

Try it at: https://github.com/RareSense/Nova3D Takes both text and image input. You'll need your own Gemini, Anthropic or Open AI key.

---

The problem it solves:
AI generators produce solid, monolithic 3D object that are great to look at but not practically super useful. Want to change the arm of a robot you generated? Re-generate the whole thing, lol.

About this tech:
It generates a Blender construction script, runs it server-side, does spatial validation, then exports a structured GLB (with the scene graph intact). Basically the output isn't only geometry. It's the procedure that built it, plus the asset itself.

In practice, this produces named parts, hierarchy preserved, material slots ready to go. And since there's a procedural script sitting in the middle, you can actually go in and edit the "kit of parts" afterwards instead of starting over.

Some generated examples:
- Internal assembly generation: https://imgur.com/a/JxDZ7Wd
- Boston Dynamics-style robot dog: https://imgur.com/a/CqMYgrF
- Microwave (random, but shows part separation well): https://imgur.com/a/hIqIJdr

Would genuinely love feedback!

Modly is an open-source desktop app for generating 3D models from images using local AI.

Why this is worth checking out:

• 100% local AI generation
• Open source
• No accounts
• No credits or token system
• No cloud upload required
• Export to common 3D formats like GLB / OBJ / STL
• Windows and Linux support already available
• Extension system for different AI models

This could be a really useful option for people who want to test image-to-3D workflows privately and freely, without getting trapped inside another “please buy more credits” ecosystem. Humanity briefly remembered software can just be software.

GitHub:
https://github.com/lightningpixel/modly

Cascadeur is a standalone AI-assisted 3D animation tool, which means no cloud-token workflow just to pose, rig, or animate your models.
Cascadeur 2026.1 just added new tools aimed at faster quadruped animation workflows.

The update includes:

• AutoPosing for faster pose blocking
• AutoPhysics to help create more natural motion
• Smart Retargeting for transferring animation between rigs more easily
• Better workflow support for animals, creatures, and four-legged characters

Quadruped animation is usually one of those tasks where every extra leg feels like a punishment from the software gods, so having AI-assisted posing, physics, and retargeting in one workflow is pretty interesting.

Could be useful for game devs, animators, creature artists, and anyone working with animals or fantasy creatures in 3D. It could also be very useful for bringing AI-generated 3D animal models to life, especially in faster game and creature workflows.

Source:
https://x.com/i/status/2053570385271013748

Cyanpuppets is showing an AI motion capture workflow for UE5.6 that can turn webcam footage or uploaded video into real-time 3D motion data.

Main points:

• Video / webcam input
• Real-time AI motion capture
• Automatic retargeting
• Native MetaHuman support
• Body, hands, and face tracking
• Runs locally with their AI model
• Designed for Unreal Engine character workflows

This kind of workflow is getting really interesting for indie game dev, previs, animation tests, VTubing, and fast character prototyping. It’s not replacing cleanup completely, because apparently suffering is still part of animation, but the speed is getting ridiculous.

Now imagine creating your own character from a single photo, then animating it with a plugin like this. You can get a pretty high-level character that is usable for games, and potentially build the whole thing by yourself in 2–3 days. That’s honestly insane.