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Unsupervised Depth Estimation, 3D Face Rotation and Replacement
[article]
2018
arXiv
pre-print
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry. We achieve this by inferring the depth of facial keypoints of an input image in an unsupervised manner, without using any form of ground-truth depth information. We show how it is possible to use these depths as intermediate computations within a new backpropable loss to predict
arXiv:1803.09202v5
fatcat:mxwesnohgjcufo4ycyx3xjumgy