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arXiv · 1904.00680

End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image

Seonghyeon Nam·Chongyang Ma·Menglei Chai·William Brendel·Ning Xu·Seon Joo Kim
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Abstract

Time-lapse videos usually contain visually appealing content but are often difficult and costly to create. In this paper, we present an end-to-end solution to synthesize a time-lapse video from a single outdoor image using deep neural networks. Our key idea is to train a conditional generative adversarial network based on existing datasets of time-lapse videos and image sequences. We propose a multi-frame joint conditional generation framework to effectively learn the correlation between the illumination change of an outdoor scene and the time of the day. We further present a multi-domain training scheme for robust training of our generative models from two datasets with different distributions and missing timestamp labels. Compared to alternative time-lapse video synthesis algorithms, our method uses the timestamp as the control variable and does not require a reference video to guide the synthesis of the final output. We conduct ablation studies to validate our algorithm and compare with state-of-the-art techniques both qualitatively and quantitatively.

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Seonghyeon Nam, Chongyang Ma, Menglei Chai, William Brendel, Ning Xu, Seon Joo Kim. 2019-04-01. End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image. https://arxiv.org/abs/1904.00680

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