3D Hand pose estimation from a single depth image is an essential topic in
computer vision and human-computer interaction. Although the rising of deep
learning method boosts the accuracy a lot, the problem is still hard to solve
due to the complex structure of the human hand. Existing methods with deep
learning either lose spatial information of hand structure or lack a direct
supervision of joint coordinates. In this paper, we propose a novel Pixel-wise
Regression method, which use spatial-form representation (SFR) and
differentiable decoder (DD) to solve the two problems. To use our method, we
build a model, in which we design a particular SFR and its correlative DD which
divided the 3D joint coordinates into two parts, plane coordinates and depth
coordinates and use two modules named Plane Regression (PR) and Depth
Regression (DR) to deal with them respectively. We conduct an ablation
experiment to show the method we proposed achieve better results than the
former methods. We also make an exploration on how different training
strategies influence the learned SFRs and results. The experiment on three
public datasets demonstrates that our model is comparable with the existing
state-of-the-art models and in one of them our model can reduce mean 3D joint
error by 25%.

本论文提出了一种基于像素回归的新方法，采用空间形态表示和可微分解码器来解决三维手部姿态估计的问题，实验结果显示本方法比现有方法更优，在三个公共数据集上将平均 3D 关节误差降低了 25％。

像素级回归：基于空间形式表示和可微分解码器的 3D 手部姿态估计

Pixel-wise Regression: 3D Hand Pose Estimation via Spatial-form  Representation and Differentiable Decoder

This paper proposes a supervised learning approach to jointly perform facial
Action Unit (AU) localisation and intensity estimation. Contrary to previous
works that try to learn an unsupervised representation of the Action Unit
regions, we propose to directly and jointly estimate all AU intensities through
heatmap regression, along with the location in the face where they cause
visible changes. Our approach aims to learn a pixel-wise regression function
returning a score per AU, which indicates an AU intensity at a given spatial
location. Heatmap regression then generates an image, or channel, per AU, in
which each pixel indicates the corresponding AU intensity. To generate the
ground-truth heatmaps for a target AU, the facial landmarks are first
estimated, and a 2D Gaussian is drawn around the points where the AU is known
to cause changes. The amplitude and size of the Gaussian is determined by the
intensity of the AU. We show that using a single Hourglass network suffices to
attain new state of the art results, demonstrating the effectiveness of such a
simple approach. The use of heatmap regression allows learning of a shared
representation between AUs without the need to rely on latent representations,
as these are implicitly learned from the data. We validate the proposed
approach on the BP4D dataset, showing a modest improvement on recent, complex,
techniques, as well as robustness against misalignment errors. Code for testing
and models will be available to download from
this https URL

本论文提出了一种监督学习方法，同时实现面部动作单位（AU）的本地化和强度估计，通过热图回归直接和联合估计所有 AU 强度和其可能导致可见变化的面部位置，旨在学习每 AU 的每个空间位置的得分。在 BP4D 数据集中，该方法取得了最新技术水平，同时对位移误差具有稳健性。

通过热图回归实现联合动作单元定位和强度估计

Joint Action Unit localisation and intensity estimation through heatmap  regression

Supervised deep learning methods have shown promising results for the task of
monocular depth estimation; but acquiring ground truth is costly, and prone to
noise as well as inaccuracies. While synthetic datasets have been used to
circumvent above problems, the resultant models do not generalize well to
natural scenes due to the inherent domain shift. Recent adversarial approaches
for domain adaption have performed well in mitigating the differences between
the source and target domains. But these methods are mostly limited to a
classification setup and do not scale well for fully-convolutional
architectures. In this work, we propose AdaDepth - an unsupervised domain
adaptation strategy for the pixel-wise regression task of monocular depth
estimation. The proposed approach is devoid of above limitations through a)
adversarial learning and b) explicit imposition of content consistency on the
adapted target representation. Our unsupervised approach performs competitively
with other established approaches on depth estimation tasks and achieves
state-of-the-art results in a semi-supervised setting.

本研究提出一个无监督领域适应策略 --AdaDepth，通过对抗式学习和对齐源域和目标域表征的显式强制内容一致性，克服了使用合成数据集时可能出现的领域适应问题，在单目深度估计任务中表现出与其他方法具有竞争力的性能并在半监督设置中实现了最先进的结果。