Learning Controllable Face Generator from Disjoint Datasets

Recently, GANs have become popular for synthesizing photorealistic facial images with desired facial attributes. However, crucial to the success of such networks is the availability of large-scale datasets that are fully-attributed, i.e., datasets in which the Cartesian product of all attribute values is present, as otherwise the learning becomes skewed. Such fully-attributed datasets are impractically expensive to collect. Many existing datasets are only partially-attributed, and do not have any subjects in common. It thus becomes important to be able to jointly learn from such datasets. In this paper, we propose a GAN-based facial image generator that can be trained on partially-attributed disjoint datasets. The key idea is to use a smaller, fully-attributed dataset to bridge the learning. Our generator (i) provides independent control of multiple attributes, and (ii) renders photorealistic facial images with target attributes.