Learned Video Compression With Residual Prediction and Feature-Aided Loop Filter

Recovering 3D geometry from a single-view 2D face image is an ill-posed task full of various challenges, especially under occlusion conditions commonly seen with large poses, while partial facial information missing makes the burden much heavier. Although existing methods could solve this problem in the end-to-end fashion, they still have limited performance in the occluded scenes. It is intuitively for many methods to depress the influence of those occluded regions for reconstruction. But we propose an occlusion-sensitive weighting mechanism for balancing the contributions among occluded and non-occluded regions. Meanwhile, considering no dataset contains various occlusions for learning, the data augmentation technique is exploited to expand the training dataset, which further facilitates the learning of the occlusion-sensitive deep network. Extensive experiments on two challenging datasets validate the advanced performance of our method for both 3D face reconstruction and face alignment.