Taking a photo outside, can we predict the immediate future, like how the cloud would move in the sky? We answer this question by presenting a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse videos of high resolution. Given the first frame, our model learns to generate long-term future frames. The first stage aims to generate videos of similar content as that in the input frame and of plausible motion dynamics. The second stage refines the generated video from the first stage by enforcing it to be closer to real videos with regard to dynamics. To further encourage realistic motion in the final generated video, Gram matrix is employed to model the motion more precisely. We build a large scale time-lapse dataset, and test our approach on this new dataset. Using our model, we are able to generate up to $128\times 128$ resolution videos for 32 frames in a single forward pass. Quantitative and qualitative experiment results demonstrate the superiority of our method over the state-of-the-art methods.

该研究基于GAN，介绍了一种生成逼真的高分辨率时间流视频的方法，第一阶段生成真实内容的视频，第二阶段利用Gram矩阵提高了运动动态和最后生成视频的逼真性。实验证明，该方法优于现有的最新模型。

利用多阶段动态生成对抗网络学习生成时间变换视频