  Kinship face synthesis is an interesting topic raised to answer questions
like "what will your future children look like?". Published approaches to this
topic are limited. Most of the existing methods train models for one-versus-one
kin relation, which only consider one parent face and one child face by
directly using an auto-encoder without any explicit control over the
resemblance of the synthesized face to the parent face. In this paper, we
propose a novel method for controllable descendant face synthesis, which models
two-versus-one kin relation between two parent faces and one child face. Our
model consists of an inheritance module and an attribute enhancement module,
where the former is designed for accurate control over the resemblance between
the synthesized face and parent faces, and the latter is designed for control
over age and gender. As there is no large scale database with
father-mother-child kinship annotation, we propose an effective strategy to
train the model without using the ground truth descendant faces. No carefully
designed image pairs are required for learning except only age and gender
labels of training faces. We conduct comprehensive experimental evaluations on
three public benchmark databases, which demonstrates encouraging results.
