Two-stage convolutional part heatmap regression for the 1st 3D face alignment in the wild (3DFAW) challenge

Abstract

This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge. Our method builds upon the idea of convolutional part heatmap regression (Bulat and Tzimiropoulos, 2016), extending it for 3D face alignment. Our method decomposes the problem into two parts: (a) X, Y (2D) estimation and (b) Z (depth) estimation. At the first stage, our method estimates the X, Y coordinates of the facial landmarks by producing a set of 2D heatmaps, one for each landmark, using convolutional part heatmap regression. Then, these heatmaps, alongside the input RGB image, are used as input to a very deep subnetwork trained via residual learning for regressing the Z coordinate. Our method ranked 1st in the 3DFAW Challenge, surpassing the second best result by more than 22%. Code can be found at http: // www.cs.nott.ac.uk/-psxab5/.