Understanding the shape of a scene from a single color image is a formidable
computer vision task. However, most methods aim to predict the geometry of
surfaces that are visible to the camera, which is of limited use when planning
paths for robots or augmented reality agents. Such agents can only move when
grounded on a traversable surface, which we define as the set of classes which
humans can also walk over, such as grass, footpaths and pavement. Models which
predict beyond the line of sight often parameterize the scene with voxels or
meshes, which can be expensive to use in machine learning frameworks.
We introduce a model to predict the geometry of both visible and occluded
traversable surfaces, given a single RGB image as input. We learn from stereo
video sequences, using camera poses, per-frame depth and semantic segmentation
to form training data, which is used to supervise an image-to-image network. We
train models from the KITTI driving dataset, the indoor Matterport dataset, and
from our own casually captured stereo footage. We find that a surprisingly low
bar for spatial coverage of training scenes is required. We validate our
algorithm against a range of strong baselines, and include an assessment of our
predictions for a path-planning task.

通过学习来自立体视频序列的训练数据，基于单个 RGB 图像预测可见和被遮挡的可通过物体表面的几何形状，所采用的图片到图片网络可有效预测用于机器人或增强现实代理的路径规划任务，并验证了预测结果。

单色图像中的足迹和自由空间

Footprints and Free Space from a Single Color Image

StyleGAN2 is a state-of-the-art network in generating realistic images.
Besides, it was explicitly trained to have disentangled directions in latent
space, which allows efficient image manipulation by varying latent factors.
Editing existing images requires embedding a given image into the latent space
of StyleGAN2. Latent code optimization via backpropagation is commonly used for
qualitative embedding of real world images, although it is prohibitively slow
for many applications. We propose a way to distill a particular image
manipulation of StyleGAN2 into image-to-image network trained in paired way.
The resulting pipeline is an alternative to existing GANs, trained on unpaired
data. We provide results of human faces' transformation: gender swap,
aging/rejuvenation, style transfer and image morphing. We show that the quality
of generation using our method is comparable to StyleGAN2 backpropagation and
current state-of-the-art methods in these particular tasks.

本文介绍了一种使用图像对图像网络训练的方式来将 StyleGAN2 的特定图像处理提炼成图像对图像的处理方式，展示了通过这种方式实现的几种人脸图像处理效果，并显示出生成质量可与 Backpropagation 方法和现有的最先进方法相媲美。