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.

本文提出了一种新的可控后代脸部合成方法，旨在通过建模两个父母脸和一个孩子脸之间的二对一亲属关系，实现合成脸与父母脸之间的准确相似度控制。通过对遗传模块和属性增强模块建模，还可控制年龄和性别。利用诸如年龄和性别标签的训练脸，我们提出了一种有效的模型训练策略。三个公共基准数据库上的全面实验评估展示了鼓舞人心的结果。

可控子孙脸生成