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That's the exact approach that I think is going to work.

Well, not exact cause I don't like their voxel building generation method.

I think a GAN + Procedural Generator is the winner.

edit: Let me know if you want to work on this cause it's an active area of research for us. See my HN profile for contact.



Can you clarify what you mean by Procedural Generator? Isn't a generative model already a procedural generator? Its just that a model generated in case of the referred paper is voxel based. Did you mean, generate parameters of a pre-specified model e.g https://graphics.ethz.ch/~edibra/Publications/HS-Nets%20-%20... , although this paper is just learning a regression to the human body model (not using GANs).

Curious to know more about your train of thought. I am working as a researcher in the domain and thinking of experimenting with GANs for 3D model estimation using similar inputs as the one in the paper I referred to.




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