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.

翻译：在这项工作中,我们提议了一个面部年龄编辑的新结构,可以产生结构上的修改,同时保留原始图像中的相关细节。我们分解输入图像的风格和内容,并提议一个新的解码器网络,采用基于风格的战略,将输入图像的风格和内容表述结合起来,同时在目标年龄调整输出。我们超越了现有的老化方法,允许用户在推断过程中调整输入图像的结构保存程度。为此,我们引入了一个掩罩机制,即CUstom结构保护模块,将输入图像中的有关区域与应当丢弃的图像区分开来。CUSP不需要额外的监督。最后,我们的定量和定性分析,包括用户研究,表明我们的方法优于先前的艺术,并展示了我们在图像编辑和可调整结构保护方面的战略的有效性。代码和预先培训的模型可在https://github.com/guillermogotre/CUSP查阅。