Nvidia tool generates full 3D models from a single still image

• HierarchicalProbabilistic3DHuman

  5 185 3.5 Python

  Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

I don't know of one yet. Lot of people are working on it, using various strategies. The grail being able to provide a virtual fitting-room experience. Or monitoring fitness of individuals.

A 2021 paper with code (I haven't tried it): https://github.com/akashsengupta1997/hierarchicalprobabilist... to generate a 3d morph-able human-shape in the correct pose from a single picture.

A picture always has scale uncertainty (a 2-meter human viewed from 1 meter away look the same that a 1-meter human viewed from 0.5 meter away), so that is an additional problem that must be taken care of.

But now recent phone have 3d sensor that provide information that could be useful.

To generate 3d human models there is also makehuman . In the old days there was a soft called facegen, that could generate 3d face models and could automatically fit their parameters to two pictures using an iterating refinement procedure.

Deep-learning usually estimate everything jointly in a single step so they are faster, but often less accurate. But there exist models that learn to refine a previously generated model, so you can apply them repeatedly and get improved quality (Denoising Diffusion Probabilistic Models is one generic class of models that does this).

• hierarchicalprobabilist

  1 - -

I don't know of one yet. Lot of people are working on it, using various strategies. The grail being able to provide a virtual fitting-room experience. Or monitoring fitness of individuals.

A 2021 paper with code (I haven't tried it): https://github.com/akashsengupta1997/hierarchicalprobabilist... to generate a 3d morph-able human-shape in the correct pose from a single picture.

A picture always has scale uncertainty (a 2-meter human viewed from 1 meter away look the same that a 1-meter human viewed from 0.5 meter away), so that is an additional problem that must be taken care of.

But now recent phone have 3d sensor that provide information that could be useful.

To generate 3d human models there is also makehuman . In the old days there was a soft called facegen, that could generate 3d face models and could automatically fit their parameters to two pictures using an iterating refinement procedure.

Deep-learning usually estimate everything jointly in a single step so they are faster, but often less accurate. But there exist models that learn to refine a previously generated model, so you can apply them repeatedly and get improved quality (Denoising Diffusion Probabilistic Models is one generic class of models that does this).

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