  This paper describes a new model which generates images in novel poses e.g.
by altering face expression and orientation, from just a few instances of a
human subject. Unlike previous approaches which require large datasets of a
specific person for training, our approach may start from a scarce set of
images, even from a single image. To this end, we introduce Character Adaptive
Identity Normalization GAN (CainGAN) which uses spatial characteristic features
extracted by an embedder and combined across source images. The identity
information is propagated throughout the network by applying conditional
normalization. After extensive adversarial training, CainGAN receives figures
of faces from a certain individual and produces new ones while preserving the
person's identity. Experimental results show that the quality of generated
images scales with the size of the input set used during inference.
Furthermore, quantitative measurements indicate that CainGAN performs better
compared to other methods when training data is limited.
