Replay Attention and Data Augmentation Network for 3D Dense Alignment and Face Reconstruction

3D face reconstruction from a single-view image in the wild is a long-standing challenging problem. Traditional 3DMM-based methods directly regressed parameters, which probably caused that the network learned the discriminative informative features in the face insufficiently. In this paper, we propose a replay attention and data augmentation network (RADAN) for 3D dense alignment and face reconstruction. Instead of the traditional attention mechanism, our replay attention module aims to increase the sensitivity of the network to informative features by adaptively recalibrating the weight response in the attention mechanism, which typically reinforces the distinguishability of the learned feature representation. In this way, the network is able to further improve the accuracy of face reconstruction and dense alignment in an unconstrained environment. Moreover, to improve the generalization performance of the model and the ability of the network to capture local details, we present a data augmentation strategy to preprocess the sample data, which generates the images that contain more local details and occluded face in a cropping and pasting manner. Extensive qualitative and quantitative experimental results on widely-evaluated benchmarking datasets demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. Code is available at https://github.com/zhouzhiyuanl/RADANet.