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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 2: AAAI-22 Technical Tracks 2

Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency

February 1, 2023

Authors

Zhenhuan Liu

Institute of Computing Technology University of Chinese Academy of Sciences


Liang Li

Institute of Computing Technology


Huajie Jiang

Huawei Technologies


Xin Jin

Huawei Technologies


Dandan Tu

Huawei Technologies


Shuhui Wang

Institute of Computing Technology


Zheng-Jun Zha

University of Science and Technology of China


Proceedings:

No. 2: AAAI-22 Technical Tracks 2

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision II

Downloads:

Download PDF

Abstract:

In recent years, creative content generations like style transfer and neural photo editing have attracted more and more attention. Among these, cartoonization of real-world scenes has promising applications in entertainment and industry. Different from image translations focusing on improving the style effect of generated images, video cartoonization has additional requirements on the temporal consistency. In this paper, we propose a spatially-adaptive semantic alignment framework with perceptual motion consistency for coherent video cartoonization in an unsupervised manner. The semantic alignment module is designed to restore deformation of semantic structure caused by spatial information lost in the encoder-decoder architecture. Furthermore, we introduce the spatio-temporal correlative map as a style-independent, global-aware regularization on perceptual motion consistency. Deriving from similarity measurement of high-level features in photo and cartoon frames, it captures global semantic information beyond raw pixel-value of optical flow. Besides, the similarity measurement disentangles temporal relationship from domain-specific style properties, which helps regularize the temporal consistency without hurting style effects of cartoon images. Qualitative and quantitative experiments demonstrate our method is able to generate highly stylistic and temporal consistent cartoon videos.

DOI:

10.1609/aaai.v36i2.20078


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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