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arXiv · 2110.11894

Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation

Jingning Xu·Benlai Tang·Mingjie Wang·Siyuan Bian·Wenyi Guo·Xiang Yin·Zejun Ma
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Abstract

Clothes style transfer for person video generation is a challenging task, due to drastic variations of intra-person appearance and video scenarios. To tackle this problem, most recent AdaIN-based architectures are proposed to extract clothes and scenario features for generation. However, these approaches suffer from being short of fine-grained details and are prone to distort the origin person. To further improve the generation performance, we propose a novel framework with disentangled multi-branch encoders and a shared decoder. Moreover, to pursue the strong video spatio-temporal consistency, an inner-frame discriminator is delicately designed with input being cross-frame difference. Besides, the proposed framework possesses the property of scenario adaptation. Extensive experiments on the TEDXPeople benchmark demonstrate the superiority of our method over state-of-the-art approaches in terms of image quality and video coherence.

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Jingning Xu, Benlai Tang, Mingjie Wang, Siyuan Bian, Wenyi Guo, Xiang Yin, Zejun Ma. 2021-10-14. Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation. https://arxiv.org/abs/2110.11894

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