  Photorealistic stylization aims to transfer the style of a reference photo
onto a content photo in a natural fashion, such that the stylized image looks
like a real photo taken by a camera. State-of-the-art methods stylize the image
locally within each matched semantic region and are prone to global color
inconsistency across semantic objects/parts, making the stylized image less
photorealistic. To tackle the challenging issues, we propose a non-local
representation scheme, constrained with a mutual affine-transfer network
(NL-MAT). Through a dictionary-based decomposition, NL-MAT is able to
successfully decouple matched non-local representations and color information
of the image pair, such that the context correspondence between the image pair
is incorporated naturally, which largely facilitates local style transfer in a
global-consistent fashion. To the best of our knowledge, this is the first
attempt to address the photorealistic stylization problem with a non-local
representation scheme, such that no additional models or steps for semantic
matching are required during stylization. Experimental results demonstrate that
the proposed method is able to generate photorealistic results with local style
transfer while preserving both the spatial structure and global color
consistency of the content image.
