We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Outof-Distribution (OOD) generalization problem. The key idea is to leverage adversarial training to create "fictitious" yet "challenging" populations, from which a model can learn to generalize with theoretical guarantees. To facilitate fast and desirable domain augmentation, we cast the model training in a meta-learning scheme and use a Wasserstein Auto-Encoder (WAE) to relax the widely used worst-case constraint. Detailed theoretical analysis is provided to testify our formulation, while extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization.

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Facial expression transfer from a single image is a challenging task and has drawn sustained attention in the fields of computer vision and computer graphics. Recently, generative adversarial nets (GANs) have provided a new approach to facial expression transfer from a single image toward target facial expressions.However, it is still difficult to obtain a sequence of smoothly changed facial expressions. We present a novel GAN-based method for generating emotional facial expression animations given a single image and several facial landmarks for the in-between stages. In particular, landmarks of other subjects are incorporated into a GAN model to control the generated facial expression from a latent space. With the trained model, high-quality face images and a smoothly changed facial expression sequence can be effectively obtained, which are showed qualitatively and quantitatively in our experiments on the Multi-PIE and CK+ data sets. KEYWORDSdynamic expression sequence, facial expression transfer, generative adversarial nets *These authors contributed equally to this work.