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.

本文介绍了一种全自动方法来将一张静态图像转换为具有连续流体运动的逼真动画循环视频。该方法依赖于单变量特征场的欧拉运动描述，并使用图像到图像的翻译网络来编码自然场景的运动先验，并通过深度扭曲技术，使用产生的运动来动画化图像，从而实现连续流畅的循环视频纹理。

使用欧拉运动场为图片添加动画

Animating Pictures with Eulerian Motion Fields

This work advocates Eulerian motion representation learning over the current
standard Lagrangian optical flow model. Eulerian motion is well captured by
using phase, as obtained by decomposing the image through a complex-steerable
pyramid. We discuss the gain of Eulerian motion in a set of practical use
cases: (i) action recognition, (ii) motion prediction in static images, (iii)
motion transfer in static images and, (iv) motion transfer in video. For each
task we motivate the phase-based direction and provide a possible approach.

本文探讨了欧拉运动在四个实际应用中（动作识别、静态图像中的运动预测、静态图像中的运动转换以及视频中的运动转换）的优点，并通过对图像进行复杂可控金字塔分解获取相位来捕获欧拉运动，而不是当前标准的拉格朗日光流模型。