  Generative adversarial networks have been widely used in image synthesis in
recent years and the quality of the generated image has been greatly improved.
However, the flexibility to control and decouple facial attributes (e.g., eyes,
nose, mouth) is still limited. In this paper, we propose a novel approach,
called ChildGAN, to generate a child's image according to the images of parents
with heredity prior. The main idea is to disentangle the latent space of a
pre-trained generation model and precisely control the face attributes of child
images with clear semantics. We use distances between face landmarks as pseudo
labels to figure out the most influential semantic vectors of the corresponding
face attributes by calculating the gradient of latent vectors to pseudo labels.
Furthermore, we disentangle the semantic vectors by weighting irrelevant
features and orthogonalizing them with Schmidt Orthogonalization. Finally, we
fuse the latent vector of the parents by leveraging the disentangled semantic
vectors under the guidance of biological genetic laws. Extensive experiments
demonstrate that our approach outperforms the existing methods with encouraging
results.
