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In recent years, creative content generations like style transfer and neuralphoto editing have attracted more and more attention. Among these,cartoonization of real-world scenes has promising applications in entertainmentand industry. Different from image translations focusing on improving the styleeffect of generated images, video cartoonization has additional requirements onthe temporal consistency. In this paper, we propose a spatially-adaptivesemantic alignment framework with perceptual motion consistency for coherentvideo cartoonization in an unsupervised manner. The semantic alignment moduleis designed to restore deformation of semantic structure caused by spatialinformation lost in the encoder-decoder architecture. Furthermore, we devisethe spatio-temporal correlative map as a style-independent, global-awareregularization on the perceptual motion consistency. Deriving from similaritymeasurement of high-level features in photo and cartoon frames, it capturesglobal semantic information beyond raw pixel-value in optical flow. Besides,the similarity measurement disentangles temporal relationships fromdomain-specific style properties, which helps regularize the temporalconsistency without hurting style effects of cartoon images. Qualitative andquantitative experiments demonstrate our method is able to generate highlystylistic and temporal consistent cartoon videos.

Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency