>If a client asks, "Can you make the handle slightly longer?", on the human model, I can select a loop of polygons and pull. The edit is done in 10 seconds.
>On the AI model, I cannot. There are no loops. I would have to sculpt it like clay, destroying the texture in the process. It is actually faster to rebuild the entire model from scratch than to try and fix the AI's topology.
To play devil's advocate for a second, it seems like you didn't provide a requirement to the AI on how the handle should be made, then got frustrated that the result doesn't conform to unspoken norms. If I made you this model by just starting with a sphere and sculpting it in ZBrush, you'd get frustrated by the same problem too.
On the other hand, I would expect that the AI could perform the task if you just elongated the handle in the reference image. The same procedure would probably work if the client wanted to add cat ears to the top to make a Mario Tennis clone game, while it might be a whole new commission for human modelers.
Now, would the material mapping still be poor, and would it be a questionable use of electricity? Guilty on both counts, but it's exciting to anyone who just wants to make 3D printed items or low-fidelity video games/mods.
Somehow this article explains perfectly, visually, how AI generated code differs from human generated code as well.
You see the exact same patterns. AI uses more code to accomplish the same thing, less efficiently.
I'm not even an AI hater. It's just a fact.
The human then has to go through and cleanup that code if you want to deliver a high-quality product.
Similarly, you can slap that AI generated 3D model right into your game engine, with its terrible topology and have it perform "ok". As you add more of these terrible models, you end up with crap performance but who cares, you delivered the game on-time right? A human can then go and slave away fixing the terrible topology and textures and take longer than they would have if the object had been modeled correctly to begin with.
The comparison of edge-loops to "high quality code" is also one that I mentally draw. High quality code can be a joy to extend and build upon.
Low quality code is like the dense mesh pictured. You have a million cross interactions and side-effects. Half the time it's easier to gut the whole thing and build a better system.
Again, I use AI models daily but AI for tools is different from AI for large products. The large products will demand the bulk of your time constantly refactoring and cleaning the code (with AI as well) -- such that you lose nearly all of the perceived speed enhancements.
That is, if you care about a high quality codebase and product...
"High-quality code can be a joy to extend and build upon." I love the analogy here. It is a perfect parallel to how a good 3D model is a delight to extend. Some of the better modelers we've worked with return a model that is so incredibly lightweight, easily modifiable, and looks like the real thing that I am amazed each time.
The good thing about 3D slop vs. code slop is that it is so much easier to spot at first glance. A sloppy model immediately looks sloppy to nearly any untrained eye. But on closer look at the mesh, UVs, and texture, a trained eye is able to spot just how sloppy it truly is. Whereas with code, the untrained eye will have no idea how bad that code truly is. And as we all know now, this is creating an insane amount of security vulnerabilities in production.
Trellis is like a year old and practically free. There are already better models to make comparisons to.
Because they all use latent diffusion, and many techniques use voxelized intermediate representations of 3d models, often generated from images, topology is bound to be bad.
There is a lot of ongoing research around getting better topology. I expect these critiques to still be valid as much as 2 years from now, but the economics of modeling will change drastically as the models get better
Everyone needs to quit trying to one-shot, and quit assuming AI can’t do it because it can’t one-shot it.
Since the author can enumerate the problems and describe them, it’d be interesting to just use the one-shot pickleball racket model as a starting point. Generate it, look at the problems, then ask an agent to build “fixers” for each problem - small scripts (that they don’t need to build themselves!) which address each problem in turn. Then send the first pass AI output through a pipeline of fix scripts to get something far better but not quite there - and do final human tuneups on the result.
That’s not really how 3D modelling works. You can’t just improve some of the model. You have to improve all of it. Fixing to top of the paddle also changes how the junction at the handle goes and so on. That’s why no one has solved ai 3D modelling yet. It’s like asking a gymnast to learn how to do the second half of a handspring first, and then for step 2 they can learn the first half. It doesn’t work like that.
The tech will catch up in a year or two. Gemini 3.1 pro can now turn a basic raster logo into fairly clean SVG. Six months ago the SOTA models where no where near completing this task.
I am in agreement with many commenters here (https://news.ycombinator.com/item?id=47158240, https://news.ycombinator.com/item?id=47158573 and others) that this article is a clear illustration of failure on part of AI to capture the structure of material in a useful way. As addressed in the article, the effect is very visible in visual space, 3D modeling. I would argue it is very much present in LLM space too, just less prominent due to certain properties of the medium - text-based language. I also believe the effect is fundamental, rooted in the design of those models.
I'll leave here the note I've written down recently, while thinking about this fundamental limitation.
- The relationship between sentient/human thinking and its expression ("language") is similar to the one between abstract/"vector" image specification and its rendered form (which is necessarily pixel-based/rasterised)
- "Truly reasoning" system operates in the abstract/"vector" space, only "rendering" into "raster" space for communication purposes. Today's LLMs, by their natural design, operate entirely in the "raster" space of (linguistic) "tokens". But from outside point of view the two are indistiguishable, superficially.
- Today's LLMs is a brute force mechanism, made possible by availability of sheer computing power and ample training material.
- The whole premise of LLMs ("Large" and "Language" being load-bearing words here) is that they completely bypass the need to formalize the "vector" part, conceptualize in useful manner. I call it "raster-vector impedance".
- Even if not formalized, it can be said that internal "structures" that form within LLM somehow encode/capture ("isomorphic to" is the word I like to use) the semantics ("vector"). I believe the same can be said about "computer vision" ML systems which learn to classify images after being fed billions of them.
- However, I believe that, by nature, such internal encoding is necessarily incomplete and maybe even incorrect.
- Despite the above, LLM can still be a useful tool in many domains. I think language translation is a task that can be very successfully performed without necessarily "decoding" the emerging underlying structures. I.e. a sentence in source language can be mapped onto a region of latent space; an isomorphic region of latent space based on target language can be used to produce an output in the target language which will be representative of an equivalent meaning, from human perspective. All without explicit conceptual decoding of underlying token weight matrices. "Black-box" translation, so to speak. I am amazed (and disturbed, and horrified too!) that producing a viable code in a programming language from casual natural language prompt turned out to be a subset of general translation task, largely. Well, at least on lower levels.
- To me it is intuitive that such design (brute-force transforms of "rasterized" data instead of explicitly conceptualizing it into "vector" forms) is very limited and, essentially, a dead-end.
This article is pretty disingenuous in the parts where it focuses on topology. CAD files are imported all the time into CG software with awful topology - looking very similar to that mess.
There's lots of software and tooling, automated and otherwise, to significantly improve topology. This is a very common problem in this space and not acknowledging that is silly. It's not perfect, and remodeling things is indeed a common solution - but retopo addons and software are big business because they're good enough for a whole lot of use cases.
The thing is CAD models look perfect. They are completely un-editable in that state however. You have to go back to the cad program to make edits to the original solid model.
Now they need to compare it with Hunyuan 3D 3.0 or other SOTA 3D generator.
Obviously it's not spewing $10,000 3D models, but results are much better than what you would get for under $500 from a human 5 years ago.
So yeah you still need human art director to make sure actual source material used for generation fits your art style, but otherwise "good enough" models are 1000 times cheaper and 10000 times faster to get.
Trellis isn't and has never been state of the art. It's not a good choice for comparison; there has been progress on a lot of these problems. There are models that can do clean topo and PBR textures, for example.
In no capacity do these create clean topo, textures, and uvs. If you do not believe me, use the reference image from the post and upload it to Meshy or Tripo and see what happens. Yes, slightly better than the open source Trellis, but still nearly impossible to work with and a model you would never put on any slightly serious eCommerce site.
We've tried them all. If one existed, it would save us money, speed up our pipeline, and trust me we'd be using it.
The close but not good enough is what gives us the illusion of productivity in this tools.
That’s why you see a a lot of hype around setups and benchmarks but not a lot of well polished products.
This article make it clear for 3d modeling, but also applies for code. Human touch is necessary for a commercial product. Otherwise it’s nothing more than a prototype.
It is actually much more difficult to maintain Ai code and 3d models than to just make your own.
Either AI can oneshot without human intervention or it becomes a pain really quickly
Don't complain about tangential annoyances, I know, I know... but how the hell am I supposed to judge the difference between the images in the post if you disabled zoom and the images are incredibly small? And when I open them in a new tab they automatically download?
On the plus side, I like the informal writing of the post. You can be serious about business and still be human
Edit: firefox reader mode works wonders on this article
The most important two words in this article are the last two: for now.
Indeed, for now generative models generate triangle soup without much thought. The same was true for 2D illustrations where generative models like Deep Dream came up with horrendous images with eyes all over, dogs with multitudes of heads and oh did I mention the eyes? That was about 10 years ago. Things changed, models improved, the eyes were tamed. Yes, people had too many or too few fingers but that also changed. From nightmare fuelling imagery with many-eyed dog heads sticking out where you don't want them to fully animated hi-res video only took a decade and things are still speeding up. The triangle soup of current 3D generative models is like the eye soup of Deep Dream, something to remember somewhat fondly which is no longer relevant now.
One flaw with this assumption is that images are available in literally counts of trillions to train on. With 3D models there are virtually no production quality models freely available to train on. Even companies like ILM or Weta have nowhere near the number of models that would be needed to train a robust modelling AI
"The 'autopsy' of 3D slop highlights a critical failure in the current AI supply chain: The Illusion of Completeness.
We are living in an era of 'Statistical Harvest' where models prioritize a 'good enough' surface over structural integrity. In the spiritual supply chain of value, this is called Cutting Corners. A 3D model that breaks down upon closer inspection lacks what I call Internal Agency—it doesn't understand the 'Seed' of its own geometry. As we move towards an agent-centric world, we must distinguish between 'Generative Noise' and 'Authentic Creation'. True value definition requires a 'Watchman' who can see beyond the first-glance polish to the underlying breakdown of utility."
I really like this framing of 'Internal Agency.' In 3D, that lack of a 'Seed' is exactly why a model fails when you try to animate it. A human modeler understands that a joint needs extra edge loops to bend correctly. It has 'intent' for the model's future. The AI, performing a 'Statistical Harvest,' only cares that the surface looks right in a static frame. It provides the 'Illusion of Completeness' but none of the functional DNA required for a production environment.
"Spot on. The 'edge loops' analogy is the perfect physical manifestation of what I mean by functional DNA.
It proves that without 'Intent for the future' (the Seed), any output is just a static corpse. In my broader framework of the Spiritual Life Archiving System, we see this everywhere: systems that look complete at a glance but lack the underlying logic to survive 'animation' or real-world pressure.
This is exactly why we need to move from Generative Slop toward Architectural Stewardship. Glad to see the 'Internal Agency' framing resonates in the 3D space."
Touché. Though if the current 'eCommerce Standard' is 'dropshipped junk that looks slightly better than a hallucination,' then I’ll happily die on the hill of being over-confident.
Nice copium. These things are going to get there fast. Even what has been shown can be a good start with a decimator at hand; We've seen this with photogrammetry before. Irony is not lost on the fact that text, which complains about it, went through AI itself.
It's not fully automated where you come up with a bunch of photos and have production assets. Never has been. It serves its purpose though, so will this if it's not already.
"We've seen this with photogrammetry before" - I do not believe we have. It's progressed but even a good scan is still not close to being something you would put on a legitimate eCommerce product page.
I honestly hope you are right and that I'm full of copium. Truly. But the progression has been nowhere near as fast as code, text, image, or video generation. And as it stood 2 years ago vs now is the same conclusion - unusable slop for most use cases.
Listen, I agree it's unusable or at least somewhat usable. As I said in another comment. Will Smith video was exactly three years ago. 3D has been a bit neglected, but it will come. I was a denier initially, but these things move real fast. Photogrammetry was never at the level of point and shoot and you have a production asset. However, it did and does serve a need and you can't deny it's not useful. It's not painless though.
That’s a fair point. I know a few foremen who use photogrammetry religiously for site surveys and volume tracking where 'lumpy' geometry doesn't matter. It’s a huge win for that niche. But yes, 3D has been lagging behind and I'm having a really hard time guestimating when it's good enough for high quality product models.
>On the AI model, I cannot. There are no loops. I would have to sculpt it like clay, destroying the texture in the process. It is actually faster to rebuild the entire model from scratch than to try and fix the AI's topology.
To play devil's advocate for a second, it seems like you didn't provide a requirement to the AI on how the handle should be made, then got frustrated that the result doesn't conform to unspoken norms. If I made you this model by just starting with a sphere and sculpting it in ZBrush, you'd get frustrated by the same problem too.
On the other hand, I would expect that the AI could perform the task if you just elongated the handle in the reference image. The same procedure would probably work if the client wanted to add cat ears to the top to make a Mario Tennis clone game, while it might be a whole new commission for human modelers.
Now, would the material mapping still be poor, and would it be a questionable use of electricity? Guilty on both counts, but it's exciting to anyone who just wants to make 3D printed items or low-fidelity video games/mods.
You see the exact same patterns. AI uses more code to accomplish the same thing, less efficiently.
I'm not even an AI hater. It's just a fact.
The human then has to go through and cleanup that code if you want to deliver a high-quality product.
Similarly, you can slap that AI generated 3D model right into your game engine, with its terrible topology and have it perform "ok". As you add more of these terrible models, you end up with crap performance but who cares, you delivered the game on-time right? A human can then go and slave away fixing the terrible topology and textures and take longer than they would have if the object had been modeled correctly to begin with.
The comparison of edge-loops to "high quality code" is also one that I mentally draw. High quality code can be a joy to extend and build upon.
Low quality code is like the dense mesh pictured. You have a million cross interactions and side-effects. Half the time it's easier to gut the whole thing and build a better system.
Again, I use AI models daily but AI for tools is different from AI for large products. The large products will demand the bulk of your time constantly refactoring and cleaning the code (with AI as well) -- such that you lose nearly all of the perceived speed enhancements.
That is, if you care about a high quality codebase and product...
The good thing about 3D slop vs. code slop is that it is so much easier to spot at first glance. A sloppy model immediately looks sloppy to nearly any untrained eye. But on closer look at the mesh, UVs, and texture, a trained eye is able to spot just how sloppy it truly is. Whereas with code, the untrained eye will have no idea how bad that code truly is. And as we all know now, this is creating an insane amount of security vulnerabilities in production.
Because they all use latent diffusion, and many techniques use voxelized intermediate representations of 3d models, often generated from images, topology is bound to be bad.
There is a lot of ongoing research around getting better topology. I expect these critiques to still be valid as much as 2 years from now, but the economics of modeling will change drastically as the models get better
Since the author can enumerate the problems and describe them, it’d be interesting to just use the one-shot pickleball racket model as a starting point. Generate it, look at the problems, then ask an agent to build “fixers” for each problem - small scripts (that they don’t need to build themselves!) which address each problem in turn. Then send the first pass AI output through a pipeline of fix scripts to get something far better but not quite there - and do final human tuneups on the result.
[1] https://en.wikipedia.org/wiki/Stone_Soup
I'll leave here the note I've written down recently, while thinking about this fundamental limitation.
- The relationship between sentient/human thinking and its expression ("language") is similar to the one between abstract/"vector" image specification and its rendered form (which is necessarily pixel-based/rasterised)
- "Truly reasoning" system operates in the abstract/"vector" space, only "rendering" into "raster" space for communication purposes. Today's LLMs, by their natural design, operate entirely in the "raster" space of (linguistic) "tokens". But from outside point of view the two are indistiguishable, superficially.
- Today's LLMs is a brute force mechanism, made possible by availability of sheer computing power and ample training material.
- The whole premise of LLMs ("Large" and "Language" being load-bearing words here) is that they completely bypass the need to formalize the "vector" part, conceptualize in useful manner. I call it "raster-vector impedance".
- Even if not formalized, it can be said that internal "structures" that form within LLM somehow encode/capture ("isomorphic to" is the word I like to use) the semantics ("vector"). I believe the same can be said about "computer vision" ML systems which learn to classify images after being fed billions of them.
- However, I believe that, by nature, such internal encoding is necessarily incomplete and maybe even incorrect.
- Despite the above, LLM can still be a useful tool in many domains. I think language translation is a task that can be very successfully performed without necessarily "decoding" the emerging underlying structures. I.e. a sentence in source language can be mapped onto a region of latent space; an isomorphic region of latent space based on target language can be used to produce an output in the target language which will be representative of an equivalent meaning, from human perspective. All without explicit conceptual decoding of underlying token weight matrices. "Black-box" translation, so to speak. I am amazed (and disturbed, and horrified too!) that producing a viable code in a programming language from casual natural language prompt turned out to be a subset of general translation task, largely. Well, at least on lower levels.
- To me it is intuitive that such design (brute-force transforms of "rasterized" data instead of explicitly conceptualizing it into "vector" forms) is very limited and, essentially, a dead-end.
There's lots of software and tooling, automated and otherwise, to significantly improve topology. This is a very common problem in this space and not acknowledging that is silly. It's not perfect, and remodeling things is indeed a common solution - but retopo addons and software are big business because they're good enough for a whole lot of use cases.
Obviously it's not spewing $10,000 3D models, but results are much better than what you would get for under $500 from a human 5 years ago.
So yeah you still need human art director to make sure actual source material used for generation fits your art style, but otherwise "good enough" models are 1000 times cheaper and 10000 times faster to get.
Unfortunately they are all proprietary, but 3D models are sort of a side area in AI research, so most of the effort is from small startups.
We've tried them all. If one existed, it would save us money, speed up our pipeline, and trust me we'd be using it.
That’s why you see a a lot of hype around setups and benchmarks but not a lot of well polished products.
This article make it clear for 3d modeling, but also applies for code. Human touch is necessary for a commercial product. Otherwise it’s nothing more than a prototype.
It is actually much more difficult to maintain Ai code and 3d models than to just make your own.
Either AI can oneshot without human intervention or it becomes a pain really quickly
On the plus side, I like the informal writing of the post. You can be serious about business and still be human
Edit: firefox reader mode works wonders on this article
Indeed, for now generative models generate triangle soup without much thought. The same was true for 2D illustrations where generative models like Deep Dream came up with horrendous images with eyes all over, dogs with multitudes of heads and oh did I mention the eyes? That was about 10 years ago. Things changed, models improved, the eyes were tamed. Yes, people had too many or too few fingers but that also changed. From nightmare fuelling imagery with many-eyed dog heads sticking out where you don't want them to fully animated hi-res video only took a decade and things are still speeding up. The triangle soup of current 3D generative models is like the eye soup of Deep Dream, something to remember somewhat fondly which is no longer relevant now.
We are living in an era of 'Statistical Harvest' where models prioritize a 'good enough' surface over structural integrity. In the spiritual supply chain of value, this is called Cutting Corners. A 3D model that breaks down upon closer inspection lacks what I call Internal Agency—it doesn't understand the 'Seed' of its own geometry. As we move towards an agent-centric world, we must distinguish between 'Generative Noise' and 'Authentic Creation'. True value definition requires a 'Watchman' who can see beyond the first-glance polish to the underlying breakdown of utility."
It proves that without 'Intent for the future' (the Seed), any output is just a static corpse. In my broader framework of the Spiritual Life Archiving System, we see this everywhere: systems that look complete at a glance but lack the underlying logic to survive 'animation' or real-world pressure.
This is exactly why we need to move from Generative Slop toward Architectural Stewardship. Glad to see the 'Internal Agency' framing resonates in the 3D space."
Nothing i tried with it got even close to th level of quality that they were advertising - felt like a bunch of examples were hand-picked, at best.
I wish I had his confidence (in eCommerce Standards)
Have we? It's still not that good.
I honestly hope you are right and that I'm full of copium. Truly. But the progression has been nowhere near as fast as code, text, image, or video generation. And as it stood 2 years ago vs now is the same conclusion - unusable slop for most use cases.
Nice copium. I've been hearing how fast these things are going to get there for a few years now.