| CPC G06T 17/20 (2013.01) [G06T 7/70 (2017.01); G06V 20/647 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30248 (2013.01)] | 20 Claims |
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1. A method comprising:
determining via a processor a correspondence between one or more two-dimensional images of a three-dimensional object and a UV representation of a three-dimensional template mesh of the three-dimensional object by training a neural network, the three-dimensional template mesh including a plurality of points in three-dimensional space and a plurality of edges between the plurality of points;
determining via the processor a deformation of the three-dimensional template mesh, the deformation displacing one or more of the plurality of points, wherein the deformation is determined so as to reduce reprojection consistency loss when mapping points from the two-dimensional images back onto the two-dimensional images through both the UV representation and the three-dimensional template mesh, wherein the one or more two-dimensional images include a proximate two-dimensional image, the proximate two-dimensional image being captured from a proximate virtual camera pose, wherein a reprojection consistency loss value depends in part on a proximate reprojection consistency loss value computed for a corresponding pixel in the proximate two-dimensional image; and
storing on a storage device a deformed three-dimensional template mesh.
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