In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so that for a new photo, we can synthesize a corresponding motion field. The image is then animated using the generated motion through a deep warping technique: pixels are encoded as deep features, those features are warped via Eulerian motion, and the resulting warped feature maps are decoded as images. In order to produce continuous, seamlessly looping video textures, we propose a novel video looping technique that flows features both forward and backward in time and then blends the results. We demonstrate the effectiveness and robustness of our method by applying it to a large collection of examples including beaches, waterfalls, and flowing rivers.

翻译：在本文中,我们展示了将静态图像转换成现实的动动动滚动视频的完全自动的方法。我们用连续流流动运动(如流水和滚动烟雾)来瞄准场景。我们的方法依据的观察是,这种自然运动可以令人信服地从静态的Eularian运动描述中复制出来,即一个单一的、时间不变的流体场,确定粒子在特定 2D 位置的即时运动。我们使用图像到图像的翻版网络来将从在线视频中收集的自然场景的先行运动进行编码,这样我们就可以合成一个相应的运动场。然后通过深层扭曲技术来利用产生的运动来进行动画动:像素被编码为深色特征,这些特征通过Eullirian 运动进行扭曲,由此产生的扭曲地貌图被解译为图像。为了产生连续、无缝循环的视频文字,我们建议一种新型的视频循环技术,在时间上和后将流动的自然场景特征进行合成。我们用一个新的视频回流技术展示了我们的方法的有效性和坚固性。我们的方法通过将它应用于一个巨大的海滩,包括大量的海滩。