More on the Blender file format: https://fossies.org/linux/blender/doc/blender_file_format/my...
The only trick is that there has to be enough Blender Python code to train the LLM on.
While it generates a lot of code that initially makes sense, when you use the code, you get a jumbled block.
But also I suspect there just isn't that much openscad code in the training data, and the semantics are different enough to python or any of the other languages that are well-represented that it struggles.
That pretty much matches my experience working with NN's and LLM's
LLMs are good at writing copy that sounds accurate and creative enough, and there are known techniques to improve that (such as generating an outline first, then generating each section separately). If you then give them a list of templates, and written examples of what they are used for, the LLM is able to pick one that's a suitable match. But this is all just probability, there's no real creativity here.
Earlier this year I played around with trying to have GPT-3 directly output an SVG given a prompt for a simple design task (a poster for a school sports day), and the results were pretty bad. It was able to generate a syntantically coreect SVG, but the design was terrible. Think using #F00 and #0F0 as colours, placing elements outside the screen boundaries, layering elements so they are overlapping.
This was before GPT-4, so it would be interesting to repeat that now. Given the success people are having with GPT-4V, I feel that it could just be a matter of needing to train a model to do this specific task.
Almost all of the generative 3D models you see are actually generative image models that essentially (very crude simplification) perform something like photogrammetry to generate a 3D model - 'does this 3D object, rendered from 25 different views, match the text prompt as evaluated by this model trained on text-image pairs'?
This is a shitty way to generate 3D models, and it's why they almost all look kind of malformed.
You wouldn't need any models to learn from. But my intuition is that RL is still quite weak, and that the model would flounder after learning to mimic background color and placing a few spheres.
Probably because they aren't as advanced and the demos aren't as impressive to nontechnical audiences who don't understand the implications: there’s lots of work on text-to-3d-model generation, and even plugins for some stable diffusion UIs (e.g., MotionDiff for ComyUI.)
For single 3D object the biggest dataset is ObjaverseXL with 10M samples
For full 3D scenes you could at best get ~1000 scenes with datasets like ScanNet I guess
Text2Image models are trained on datasets with 5 billion samples
Got a public dataset?
I've not worked in VFX for a while, but when I did the modeling departments at multiple studios had giant libraries of completed geometries for every project they ever did, plus even larger libraries of all the pieces and parts they use as generic lego geometry whenever they need something new.
Every 3D modeler I know has their own personal libraries of things they'd made as well as their own "lego sets" of pieces and parts and generative geometry tools they use when making new things.
Now this is just a guess, but do you know anyone going through one of those video game schools? I wager the schools have big model libraries for the students as well. Hell, I bet Ringling and Sheridan (the two Harvards of Animation) have colossally sized model libraries for use by their students. Contact them.
So we could have something to convert AI-generated image output into 3D scenes without having to explicitly train the "creative" AI for that.
Probably much more viable, because the quantity of 3D models out in the wild is far far lower than that of bitmap images.
I'm always confused why I don't hear more about projects going in this direction. Controlnets are great, but there's still quite a lot of hallucination and other tiny mistakes that a skilled human would never make.