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 [1], 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/
Bulat and Tzimiropoulos REF applied convolutional heatmap regression to 3D face alignment, by estimating the 2D coordinates of the facial landmarks using a set of 2D heatmaps, one per landmark, estimated using a CNN with an L 2 regression loss.
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
ArXiv
ArXiv
Computer Science