u/mhb_11

Unitree G1 in the MuJoCo copy of my messy apartment, real time. Walking is Unitree's policy, arms are scripted. Yes, it knocks the bunny off the counter at the end.

I've been building a tool that generates digital twins/cousins of real spaces for robot simulation. This is a test of it. The first photo is my messy living room. The next one is the same angle in MuJoCo (with a Go2). Keep swiping to see more.

What is this showcase:
I'm showcasing the digital cousin of my messy apartment (and not the robots). To try it out, I then had a Unitree G1 pick up a toy and drop it into a bin, and pick up a mug from my coffee table and carry it to the kitchen counter. All running in MuJoCo, in real time. What's ours and what isn't: the environment is ours. The walking is Unitree's trained policy. The arm motions are scripted. It's one simulation, and I moved the robot to a new start spot for each of the four shots.

How I created the environment:
I took 8 photos on my phone and our tool built the scene from them. 286 separate parts, each with its own mass and friction, and 30 working joints. Photo inputs are what I used here. But it also takes video and scans.

You can use this environment generation tool right now:
The tool is called Nova3D and is live at: https://studio.nova3d.xyz/robotics
It runs in a browser for free on the Claude or Codex you already have. And there's nothing to install. It isn't open source yet. It works like a chat: upload your photos and say what you want. It has internet access, so it will generate well-known landmarks for you anyway. My messy apartment's digital cousin took ~40 mins. I used opus5.5, and the build fit inside my $20 subscription's 5-hr session limit.

What's a digital cousin?
I call it a 'cousin' because the dimensions are estimated from photos. That may be enough. Stanford's ACDC paper found that policies trained on close copies of a scene transferred to the real one better than policies trained on an exact twin (90% vs 25% in their tests). We haven't benchmarked ours on a real robot yet.

Our origin:
The approach comes from our paper (arXiv, not yet peer reviewed): https://arxiv.org/abs/2607.22738

If you train robots for homes, offices or warehouses:
You can generate your own environment with the tool's link above. If you'd rather I do it, send me photos and tell me which simulator you use. I'll build the first 5 for free, with the same tool. One question for you: is a close copy enough for your work, or do you need measured accuracy?

Hey everyone! Creating a CAD from client-provided images is a laborious process, especially when the design is unique.

So I've been building an AI to automate the initial conversion.

In the video, I show an elephant and peacock ring render converted to a CAD via my AI. There was no human intervention.

Can you please provide feedback? I'm trying to improve the results.
Even if not 100% accurate, I want to get it to a point where a human technician can use the results and get a big efficiency boost.

p.s. if you're curious, you can see more about my tool at https://formanova.ai/ai-jewelry-cad/

Testing an automated blockout pipeline for counter sketches to cut down Rhino massing time. Looking for 5 bench jewelers who want to test the NURBS output on their actual casting setups.

Output in native .3dm format

Hey everyone! Generating a CAD from client-provided images is always a laborious process.

So I've been trying to automate that using AI algorithms.

The example I've shared is an actual output from my tool (extracted yesterday). Would love to get a critique of what it got right/wrong in this example.

Trying to improve it for production. It's not 100% accurate, but I believe it can make existing workflows faster.

Note: if anyone is curious, you can see more about my tool at https://formanova.ai/ai-jewelry-cad/

AI can generate 3D models, but it produces monolithic mesh blobs that require extra time to convert into usable, hierarchical parts for downstream workflows.

However, I've created an approach where all objects are composed of separate, logical parts and hence are very easy to animate. E.g. my video shows generating a:
(i) 0:00 - crab-like robot, with an animation-ready body,
(ii) 0:11 - banana car, with wheels that spin and steer,
(iii) 0:22 - sushi dragon, spiraling in a corkscrew pattern,
(iv) 0:33 - alien jello, that jiggles and wiggles,
(v) 0:52 - astronomer's house, with multiple storeys, where you can walk up the stairs (1:06)

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 break down the exact procedure under the hood in this arXiv paper I co-authored: https://arxiv.org/abs/2607.22738

Web App:
Check out the web app at: https://nova3d.xyz
Scroll down to see the various types of 3D assets it can produce. A more expansive list is here: https://app.nova3d.xyz/showcase

I've designed products before but not really a jewelry designer. I used an AI algorithm to generate this NURBS Geometry. I was fascinated by the results, hence sharing it here.

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

I'm trying to create the most nepo team of the current NBA era. So I just traded for Bronny James (PG) and Thanasis Antetokounmpo (SF).

Who're my other 3 starters? Give me your best frontoffice family-hire suggestions.