  This paper focuses on the problem of generating human face pictures from
specific attributes. The existing CNN-based face generation models, however,
either ignore the identity of the generated face or fail to preserve the
identity of the reference face image. Here we address this problem from the
view of optimization, and suggest an optimization model to generate human face
with the given attributes while keeping the identity of the reference image.
The attributes can be obtained from the attribute-guided image or by tuning the
attribute features of the reference image. With the deep convolutional network
"VGG-Face", the loss is defined on the convolutional feature maps. We then
apply the gradient decent algorithm to solve this optimization problem. The
results validate the effectiveness of our method for attribute driven and
identity-preserving face generation.
