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

Many applications, such as autonomous driving, heavily rely on multi-modal
data where spatial alignment between the modalities is required. Most
multi-modal registration methods struggle computing the spatial correspondence
between the images using prevalent cross-modality similarity measures. In this
work, we bypass the difficulties of developing cross-modality similarity
measures, by training an image-to-image translation network on the two input
modalities. This learned translation allows training the registration network
using simple and reliable mono-modality metrics. We perform multi-modal
registration using two networks - a spatial transformation network and a
translation network. We show that by encouraging our translation network to be
geometry preserving, we manage to train an accurate spatial transformation
network. Compared to state-of-the-art multi-modal methods our presented method
is unsupervised, requiring no pairs of aligned modalities for training, and can
be adapted to any pair of modalities. We evaluate our method quantitatively and
qualitatively on commercial datasets, showing that it performs well on several
modalities and achieves accurate alignment.

本研究提出一种利用图像到图像翻译网络训练形态转换网络的多模态配准方法，通过单模态指标对图像进行训练，无需对齐模态对即可进行训练，适用于多种模态，实验结果表明该方法高度准确。

无监督多模式图像配准的几何保持图像到图像翻译

Unsupervised Multi-Modal Image Registration via Geometry Preserving  Image-to-Image Translation

It has been recently shown that neural networks can recover the geometric
structure of a face from a single given image. A common denominator of most
existing face geometry reconstruction methods is the restriction of the
solution space to some low-dimensional subspace. While such a model
significantly simplifies the reconstruction problem, it is inherently limited
in its expressiveness. As an alternative, we propose an Image-to-Image
translation network that jointly maps the input image to a depth image and a
facial correspondence map. This explicit pixel-based mapping can then be
utilized to provide high quality reconstructions of diverse faces under extreme
expressions, using a purely geometric refinement process. In the spirit of
recent approaches, the network is trained only with synthetic data, and is then
evaluated on in-the-wild facial images. Both qualitative and quantitative
analyses demonstrate the accuracy and the robustness of our approach.

使用 Image-to-Image 翻译网络将输入图像及其深度图及面部对应图映射起来，可通过几何细化过程提供多种严格的人脸重建，只需在合成数据上训练即可对本地面部图像进行精确和鲁棒的重建。