I tried all the recent wave of text/image to 3D model services, some touting 100 MM+ valuations and tens of millions raised and found them all to produce unusable garbage.
You have to look at this as stepping stone research.
The potential target market is significantly different in scale (I assume, I haven't tried to estimate either). The potential competitors are... already in existence. It seems more likely now that we'll succeed at good 3d-generative-AI then it seemed before we got good 2d-generative-AI that we would succeed at that...
The problem is that the output you get is just baked meshes. If the object connects together or has a few pieces you'll have to essentially undo some of that work. Similar problems with textures as the AI doesn't work normally like other artists do.
All of this is also on top of the output being basically garbage. Input photos ultimately fail in ways that would require so much work to fix it invalidates the concept. By the time you start to get something approaching decent output you've put in more work or money than just having someone make it to begin with while essentially also losing all control over the art pipeline.
[1]: https://github.com/CLAY-3D/OpenCLAY
If by "hold for replication outside the specific circumstances of one study" you mean "useful for real world problems" as implied by your previous comment then I don't think you are correct.
From a quick search it seems there are multiple definitions of Reproducibility and Replicability with some using the words interchangeably but the most favorable one I found to what you are saying is this definition:
>Replicability is obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data.
>[...]
>In general, whenever new data are obtained that constitute the results of a study aimed at answering the same scientific question as another study, the degree of consistency of the results from the two studies constitutes their degree of replication.[0]
However I think this holds true for a lot of ML research going on. The issue is not that the solutions do not generalize. It's that the solution itself is not useful for most real world applications. I don't see what replicability has to do with it. you can train a given model with a different but similar dataset and you will get the same quality non-useful results. I'm not sure exactly what definition of replicability you are using though if there is one I missed please point it out.
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