Uncertainty-Aware Semi-Supervised Learning of 3D Face Rigging from Single Image

We present a method to rig 3D faces via Action Units (AUs), viewpoint and light direction, from single input image. Existing 3D methods for face synthesis and animation rely heavily on 3D morphable model (3DMM), which was built on 3D data and cannot provide intuitive expression parameters, while AU-driven 2D methods cannot handle head pose and lighting effect. We bridge the gap by integrating a recent 3D reconstruction method with 2D AU-driven method in a semi-supervised fashion. Built upon the auto-encoding 3D face reconstruction model that decouples depth, albedo, viewpoint and light without any supervision, we further decouple expression from identity for depth and albedo with a novel conditional feature translation module and pretrained critics for AU intensity estimation and image classification. Novel objective functions are designed using unlabeled in-the-wild images and in-door images with AU labels. We also leverage uncertainty losses to model the probably changing AU region of images as input noise for synthesis, and model the noisy AU intensity labels for intensity estimation of the AU critic. Experiments with face editing and animation on four datasets show that, compared with six state-of-the-art methods, our proposed method is superior and effective on expression consistency, identity similarity and pose similarity.