CONTROLLABLE IMAGE SYNTHESIS WITH ATTRIBUTE-DECOMPOSED GAN

Abstract

Controllable Image Synthesis With Attribute-Decomposed GAN (AD-GAN) proposes
a novel generative adversarial network framework that facilitates precise control over
image synthesis by decomposing attributes into separate components. This model
introduces an innovative method of disentangling image attributes, allowing for
individual modification of specific features without affecting others. By utilizing an
attribute-decomposed representation, AD-GAN effectively isolates various elements,
such as pose, expression, and identity in facial images, enabling the generation of
highly realistic and customizable images. This approach significantly enhances the
flexibility and accuracy of image generation tasks, making it a valuable tool for
applications requiring detailed attribute manipulation.