DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

Understand

In this work we use deep learning to establish dense correspondences between a 3D object model and an image "in the wild".

• We introduce "DenseReg", a fully-convolutional neural network (F-CNN) that densely regresses at every foreground pixel a pair of U-V template coordinates in a single feedforward pass.
• To train DenseReg we construct a supervision signal by combining 3D deformable model fitting and 2D landmark annotations.
• We define the regression task in terms of the intrinsic, U-V coordinates of a 3D deformable model that is brought into correspondence with image instances at training time.