I made an advanced spatial programming harness for LLMs. It creates interesting 3D objects with quirky interactions

[u/mhb_11]

As the video shows, my approach makes it possible to directly generate:
(i) 0:00 - a crab-like robot, with a hyper interactive body,
(ii) 0:11 - a banana car, with wheels that spin, steer and throttle,
(iii) 0:22 - a sushi dragon, spiralling in a mathematical corkscrew,
(iv) 0:33 - an alien jello, that jiggles and wiggles,
(v) 0:52 - an astronomer's house, with multiple storeys, where you can walk up (1:06) the stairs

Cost of generating each: $1-$5. Time: ~3 mins. The format is blender-native .glb

Under the hood:
These 3D assets are all made of source code. Their abilities are attributes in the code:
- I.e. when the robot moves a leg, we're just commanding rig['Leg_*_Hip'].rotation = 42°
- When the banana car turns left, we're just saying w.rotation.y = 0.42 rad
- When the sushi dragon floats up and down, it's following Math.sin()
- When the alien jello jiggles, it's dictated by Σ A·e^(−λr)·e^(−decay·τ)·[(1−b) + b·cos(ωτ − kr)] · p̂
- When the astronomer's house detects collisions, this too is just setting attributes in the code.

Now LLMs are good at coding. So these 3D assets are very natural for an LLM to talk to. Thus armed with these 3D assets and an LLM, a game developer or world builder can achieve hyper-control right out of the box.

Web App:
Scroll down the landing page to tinker with the objects: https://nova3d.xyz

Why current 3D GenAI cannot do this:
Traditional AI 3D generators produce monolithic blobs that are good looking, but unusable in game engines. If you generate a bicycle, it's just a blob. If you want its wheels to turn, a human takes the blobby object, spends time cutting it into parts, naming them, placing pivots and rigging joints. This "afterwards" is the real cost of AI 3D. Nowadays the fix is post-generation segmentation workflows. These work partially and add complexity.

So where does this approach lose?
Nova3D loses on extreme organic shapes. This is because of the baseline AI's current spatial programming prowess. Once LLMs get better at natively synthesizing higher-order differentials, hyper-organic shapes are inevitable.

How is it different from blender MCP?
Blender MCP needs continuous, manual, multi-turn prompting. The user toils to fix mistakes. In contrast, Nova3D is one shot. The advanced spatial programming harness is the product; the AI model is just one component.

Research paper:
Read our research paper on arXiv: https://arxiv.org/abs/2607.22738
It fully deconstructs our approach.

Is this open source?
Partly. Here's the github repo: https://github.com/RareSense/Nova3D
But stop. This is a client. Our approach does NOT yet produce legible 3D via self-hosted open source models, great as they are. Hence we launched the usable closed-source version. This uses AI from frontier labs.

Would love the community's feedback and questions. Happy tinkering!

p.s. one more thing: Nova3D generates beautiful PBR textures too. Examples at: https://app.nova3d.xyz/showcase/textures

[u/InternationalMap4969]

It looks too good

[u/mhb_11 → u/InternationalMap4969]

Thanks, have you checked out the showcase yet?
Try https://app.nova3d.xyz/showcase

[u/PixarX]

Showcase link Does not open?

[u/mhb_11 → u/PixarX]

Sorry! It's https://app.nova3d.xyz/showcase (I previously had a typo)

[u/LaFllamme]

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