Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation

TL;DR

This paper presents an optimization-based convolutional network approach for generating human face images that accurately reflect specified attributes while preserving the identity of a reference face, addressing limitations of previous models.

Contribution

It introduces a novel optimization model using deep features for attribute-driven, identity-preserving face generation, leveraging gradient descent on VGG-Face features.

Findings

Effective preservation of identity in generated faces

Successful attribute manipulation in face images

Validation of the method's effectiveness through experiments

Abstract

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.