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.
@article{arxiv.1904.00680,
title = {End-to-End Time-Lapse Video Synthesis from a Single Outdoor Image},
author = {Seonghyeon Nam and Chongyang Ma and Menglei Chai and William Brendel and Ning Xu and Seon Joo Kim},
journal= {arXiv preprint arXiv:1904.00680},
year = {2019}
}