I think it can actually be a bit better in terms of artistic consistency to have a fixed set of materials you can work with in a given game. Very important if you want any sort of stylized rendering!
2,421 karma · joined July 7, 2021
The scene is only going to be loaded / unloaded all at once, you can just load the data into contiguous arrays and index from them. No need to use shared_ptr since lifetimes aren't that complex.
Ah... sweet summer child.
> Intellectual property theft. How can we be sure that no output of our American frontier AI models was used while training these Chinese models?
The US AI models are already using pirated copyrighted material off the Internet. If Chinese models also do this, they're at least giving it back to the people by releasing their weights as open source.
Template metaprogramming is sometimes very useful to get around C++'s language restrictions, but I tend to use it sparingly.
Or if you're even lazier, you can easily make a more automatic RTTI system with some templates / macros that works much faster than dynamic_cast! (See https://github.com/royvandam/rtti)
The world we live in is already dead, and we are wandering with its ghosts. After capitalism strips everything off its materiality and leaves only with its symbols, only the nostalgia for a non-existent past remains.
Vanilla 3DGS cannot do any specular lighting or reflections - the color is basically baked in the splats. There's some active research going on to create richer Gaussian splats so we can do shading (or even ray tracing on it) - but haven't seen anyone using in production yet.
The contributions of 3DGS lie in how fast you can make them in modern GPU hardware (tiling + sorting with threads), and how to make the pipeline differentiable so that you can fit the Gaussian splats with photogrammetry data. Similar to the history of deep learning, it became technically feasible once the GPU hardware was powerful enough.
I think future papers would probably continue improving on this method and focus on how to sample the points more efficiently while being unbiased (similar to how ray-tracing solved their performance issues). Or maybe... we can just add a deep-learning based denoiser and call it a day!
Some gamedevs hoped Nanite would eliminate the need for rasterization hardware, but because it's only performant in certain scenarios (very high poly meshes) consumers are pushing back against this.
Or well... since we now have Claude I might have a jab at this someday in my free time.
Open source models currently look good on paper, but the flagship models are still quite huge and cannot be run efficiently on consumer hardware, thus having the same inference hosting cost issues (I'm assuming OpenAI and Anthropic has done a shit ton of optimizations already on their servers.) The main breakthrough needed is plainly cheaper and more efficient hardware (such as TPUs or Huawei Ascends), and how to scale infrastructure quickly without losing money.
Mesh processing is a very difficult research domain in computer graphics that has been iterated for several decades, and we still don't have a good automated solution for retopology (Partly because the problem is hard to define in a mathematical way, but also since it's not a problem you can just solve with AI by throwing data and compute at it)
Aligned with the theory of Bullshit Jobs - LLMs expose the fact that the white collar work most of us have been doing at this point were actually bullshit. When LLMs "fake" work, it actually hides the reality that there was no meaningful work here in the first place.
It definitely still is a hardware problem today - humanoids force you to miniaturize gears, motors, and other parts (especially with the hands) which make them incredibly fragile and inaccurate. You're basically fighting against the laws of physics, so improvement on this front has been pretty slow. And tactile sensors which are key for complex manipulation tasks are still a far way to go in terms of resolution and reliability - so most robotics startups tend to rely on cameras for everything.
I think that in order to have humanoids that are actually capable of matching or exceeding the actual mechanical capability of humans, you need large advancements not just in AI but in material science as well - no machine can still match the efficiency of humans with its biological muscles, tendons, the skin / fat that surrounds them, and its vast array of sensory input.