Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency

TL;DR

This paper introduces an unsupervised framework for coherent video cartoonization that maintains temporal consistency and style quality using perceptual motion and semantic alignment techniques.

Contribution

It proposes a novel spatially-adaptive semantic alignment and a style-independent regularization method for unsupervised coherent video cartoonization.

Findings

Achieves highly stylistic cartoon videos with temporal consistency

Outperforms existing methods in qualitative and quantitative evaluations

Effectively disentangles style from temporal information

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 devise the spatio-temporal correlative map as a style-independent, global-aware regularization on the 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 in optical flow. Besides, the similarity measurement disentangles temporal relationships 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.