  In this work, we propose a novel architecture for face age editing that can
produce structural modifications while maintaining relevant details present in
the original image. We disentangle the style and content of the input image and
propose a new decoder network that adopts a style-based strategy to combine the
style and content representations of the input image while conditioning the
output on the target age. We go beyond existing aging methods allowing users to
adjust the degree of structure preservation in the input image during
inference. To this purpose, we introduce a masking mechanism, the CUstom
Structure Preservation module, that distinguishes relevant regions in the input
image from those that should be discarded. CUSP requires no additional
supervision. Finally, our quantitative and qualitative analysis which include a
user study, show that our method outperforms prior art and demonstrates the
effectiveness of our strategy regarding image editing and adjustable structure
preservation. Code and pretrained models are available at
https://github.com/guillermogotre/CUSP.
