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.

利用深度神经网络训练出的有条件生成对抗网络(ECGAN)可以从单幅室外图像中合成出一段时间流逝的视频，同时提出了多帧联合条件生成框架以有效地学习室外场景中的照明变化与白天时间之间的相关性，并没有参考视频用于合成最终输出，与替代解决方案相比具有更好的效果。

从单张户外图像生成端到端延时视频