Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning

• URL: http://arxiv.org/abs/2004.06502v1
• Date: Tue, 14 Apr 2020 13:44:30 GMT
• Title: Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning
• Authors: Kangning Liu, Shuhang Gu, Andres Romero, Radu Timofte
• Abstract summary: We propose a novel unsupervised video-to-video translation model. Our model decomposes the style and the content using the specialized UV-decoder structure. Our model can produce photo-realistic videos in a multimodal way.
• Score: 92.17835753226333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent. In this work, we propose UVIT, a novel unsupervised video-to-video translation model. Our model decomposes the style and the content, uses the specialized encoder-decoder structure and propagates the inter-frame information through bidirectional recurrent neural network (RNN) units. The style-content decomposition mechanism enables us to achieve style consistent video translation results as well as provides us with a good interface for modality flexible translation. In addition, by changing the input frames and style codes incorporated in our translation, we propose a video interpolation loss, which captures temporal information within the sequence to train our building blocks in a self-supervised manner. Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way. Subjective and objective experimental results validate the superiority of our model over existing methods. More details can be found on our project website: https://uvit.netlify.com

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