Exemplar-based Generative Facial Editing
This work addresses facial editing for users needing personalized image manipulation, but it appears incremental as it builds on existing generative models.
The paper tackles facial editing by proposing an exemplar-based generative approach that masks editing regions and learns from reference images, achieving diverse and personalized results with enhanced user control.
Image synthesis has witnessed substantial progress due to the increasing power of generative model. This paper we propose a novel generative approach for exemplar based facial editing in the form of the region inpainting. Our method first masks the facial editing region to eliminates the pixel constraints of the original image, then exemplar based facial editing can be achieved by learning the corresponding information from the reference image to complete the masked region. In additional, we impose the attribute labels constraint to model disentangled encodings in order to avoid undesired information being transferred from the exemplar to the original image editing region. Experimental results demonstrate our method can produce diverse and personalized face editing results and provide far more user control flexibility than nearly all existing methods.