Actually sorry not sure if they use triangle splatting, but gaussian splats at least.
Media Molecule (LittleBigPlanet) spent over 8 years creating Dreams, which would never have happened without Sony's funding, but the tragedy was forcing what could have been a brilliant high-end authoring tool into a gamepad / lean-back interface. Even now, there are still innovative UX approaches in Dreams that could be adapted into high-end 2D, 3D, compositing and animation tools like Blender.
At one point it used triangles for the large splats, and still compute shaders for the small ones due to quad overdraw. I'm not sure if the final version stuck with that or went all compute, but quad complexity is also why nanite does compute shaders for small triangles and traditional rasterization for the larger ones.
But it's a bit difficult to find the exact reason since profilers won't really tell you a concrete bottleneck when drawing vast amounts of tiny tris.
Things like triangle splatting are useful if you have to integrate into an engine that only support triangles. But with modern graphics pipelines, you can implement Gaussians quite efficiently, including newer techniques like Gaussian point splatting that can scale up seamlessly to enormous scenes:
* Video: https://www.youtube.com/watch?v=2AWv_nIfp-U
* Paper: https://jorisar.nl/gaussian_point_splatting/gaussian_point_s...
runs on a phone: https://storage.googleapis.com/jax3d-public/projects/mobilen...
To be clear, I'm not proficient in it's use, just watched a few videos about it's theory when it was initially announced
But again, I've really only super superficial understanding of this, I just understood it like that back then
I learned about gaussian splattering mere months before nanite was introduced and their technical talks and articles always sounded exactly like what I just learned about the splattering, that's why I got that impression
It took me a while to properly understand it, but it's genuinely great. It maps so much more of what I expect from traditional coding, scripting etc into the visual paradigm and makes an awful lot easier to use.
Also, should out to databpy and nodebpy, both of which are indispensable and make using nodes and blender so much nicer:
https://github.com/BradyAJohnston/databpy https://github.com/BradyAJohnston/nodebpy
They have easy subscription options, even a monthly coffee adds up! Thanks!
Their LazyPosy channel has a video about how they made this one, difficulties, etc if you're interested in that too. More practical than technical, though it wasn't done with Blender.
Thanks!
He is very frequently posting news about the splat world as well as quite many tests and demos with different software or hardware. I would expect him to cover Blender specific stuff soon, but it's a great source already for existing stuff.
Claude Opus is very good at creating geometry nodes for specific applications. You have to know what to ask for. It's not a magic genie.
For example, I asked it to make a node setup to create the geometry for a table. It did a mediocre job of it. Then I told it to do research on the nomenclature for tables and create a parametric control interface to generate tables with the UI using the table nomenclature. It did a very good job of that. It set it up so that I could put the units in using Imperial or metric. I also asked it to come up with reasonable default values for the parameters and it did. With just a little tweaking I have a very useful table generator for greyboxing scenes.
I then did a similar round with it for book cases and straightback chairs with useful results.
I asked it to make material nodes for wood: oak, maple, etc. And it did a pretty good job of that. Nothing amazing, but a useful place to start.
It won't be replacing people that know what they are doing, but its useful for giving that person better specifications for what you want.
I had Claude keep notes on the project and it made a skill that has been useful for later projects.
Think of it as a very bright intern that works very fast but has very little practical experience.
The problem, of course, is that if AI replaces interns, then there may not be a future generation of experts. By then, though, the AIs may be good enough to replace the experts. Next year? Next week? Who knows. If you guide it right to learn as it works, it _does_ get better over time.
I have no idea what to expect or what the actual limits of this tech is. I've see it make really hilarious mistakes, then turn around and explain the math its using so well that I can ask for what I want "properly" and it's surprised that that was what I really wanted.
It is like dealing with a very well read alien.
If the AIs don't end up murdering us all in our sleep it could be an amazing world we are entering.
Lately, I've been pondering this question in general and I'm worried the answer might be "Never" for some kinds of work - at least if we don't change how LLMs are currently developed and improved. At a high-level, the problem is they don't 'learn on the job' like a human and writing their memory & learning in .md files works for some things but fails for others.
While the 'rising tide' of baseline LLM knowledge continues to grow, they are still FTIs (Factory Trained Intelligences), meaning they mostly learn from data which can be assembled/tagged or synthesized in mass bulk - plus specific abilities in certain domains which the factory has prioritized for more costly special training. For some broad categories of important work, I suspect there's a significant unfilled gap in our LLM instances being able to effectively crystallize knowledge acquired on-task. Without that, context files will remain helpful crutches not true substitutes.
Sci-fi stuff aside, it's not just a matter of the AI's telling the supporting tech what to do. We are already at the point where we can't predict what the AI will do to the supporting tech. Blender, for example, is mostly traditional computer science with some physics and art direction thrown in. It's damn hard to use.
This is not a criticism, that's just where we are in developing a UI for the math and physics and art direction. Just using the AI to make for me UI changes, convenience features, and special utilities has drastically changed not only the way I work, but the kind of projects I'll undertake. I think nothing of having Claude whip up a special physics based particle system or compositing stack these days. I could have done that in the past, but it would have been so much work for a one time job I wouldn't consider it. I'd just brute force a solution with manual keyframes and a lot of trial and error.
And while I can program and solve (simple) physics problems, its not my day job. My day job is telling stories and explaining things with animated narratives.
Earlier today I was asked to help with some animations explaining how musical scales worked and to come up with ways to visualize them. Using Claude I've already got Blender doing a credible job at creating musical notation and linking it up to a (sort of) physically accurate sound wave simulation/visualization. Not done yet, but it's looking promising. This is within a few tens of minutes of an instructional designer asking me what might be possible for the topic. And I already have a sound synthesizer written in javascript (by Claude) that I used for a project a while back. Hooking them together is just a json file or two away...
Even if the Super Intelligence never materializes, just using current LLMs as an amplifier for existing technology is going to change things into something unrecognizable. And something that is very dangerous in the hands of human bad actors.
I didn't discuss how I think the tech will change. I pointed out a current gap in crystalizing knowledge acquisition gained on-task because I feel that gap is still under-appreciated. There's a lot of very interesting work trying to identify how to cost effectively narrow that gap (Dynamic Modular Adapters, Background Distillation, Test-Time Training, etc).
They all can be effective to varying degrees in different ways but they also all have their own gaps and trade-offs. As of now, there's no clear path to per-instance knowledge crystalization that's both broadly generalizable and scales cost-effectively. Will both be solved eventually? I think almost certainly. Will both be solved in the next 2-5 years? Maybe... but it smells like the kind of fundamental problem that's 'wicked' hard, so I wouldn't be surprised if the LLM you and I can afford to daily drive in 2 - 5 years is far more capable and 'smart' but isn't that much better at automatic crystalized learning from on-task knowledge acquisition than the best LLMs are today.
To be clear, LLMs are fabulously useful in myriad ways and I think they'll continue getting much more useful in many more ways. That's so much a given, it makes identifying which remaining gaps are so fundamental there's a good chance they won't be solved in 2-5 years especially interesting.
>Lately, I've been pondering this question in general and I'm worried the answer might be "Never" for some kinds of work
Why are we worried that AIs might not be able to replace people? That seems like quite the dystopian statement.
Though with some graphic design tools you have to proxy it with AppleScript because there is no other automatable interface with the design tool.