Estimating 3D poses and shapes in the form of meshes from monocular RGB
images is challenging. Obviously, it is more difficult than estimating 3D poses
only in the form of skeletons or heatmaps. When interacting persons are
involved, the 3D mesh reconstruction becomes more challenging due to the
ambiguity introduced by person-to-person occlusions. To tackle the challenges,
we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics
from the occlusion-robust 3D skeleton estimation and 2) Transformer-based
relation-aware refinement techniques. In our pipeline, we first obtain
occlusion-robust 3D skeletons for multiple persons from an RGB image. Then, we
apply inverse kinematics to convert the estimated skeletons to deformable 3D
mesh parameters. Finally, we apply the Transformer-based mesh refinement that
refines the obtained mesh parameters considering intra- and inter-person
relations of 3D meshes. Via extensive experiments, we demonstrate the
effectiveness of our method, outperforming state-of-the-arts on 3DPW, MuPoTS
and AGORA datasets.

本研究提出了一种粗到细的流程来解决从单眼 RGB 图像中估计多人物 3D 姿态和形状网格的问题，并且通过 Transformer-based refinement 和克服因人与人之间的遮挡而引入的不确定性，优于现有技术，在 3DPW，MuPoTS 和 AGORA 数据集上进行广泛实验得出了实验证明。

逆向运动学与细化方法实现多人 3D 姿态和形状估计

Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and  Refinement

Occlusion and pose variations, which can change facial appearance
significantly, are two major obstacles for automatic Facial Expression
Recognition (FER). Though automatic FER has made substantial progresses in the
past few decades, occlusion-robust and pose-invariant issues of FER have
received relatively less attention, especially in real-world scenarios. This
paper addresses the real-world pose and occlusion robust FER problem with
three-fold contributions. First, to stimulate the research of FER under
real-world occlusions and variant poses, we build several in-the-wild facial
expression datasets with manual annotations for the community. Second, we
propose a novel Region Attention Network (RAN), to adaptively capture the
importance of facial regions for occlusion and pose variant FER. The RAN
aggregates and embeds varied number of region features produced by a backbone
convolutional neural network into a compact fixed-length representation. Last,
inspired by the fact that facial expressions are mainly defined by facial
action units, we propose a region biased loss to encourage high attention
weights for the most important regions. We validate our RAN and region biased
loss on both our built test datasets and four popular datasets: FERPlus,
AffectNet, RAF-DB, and SFEW. Extensive experiments show that our RAN and region
biased loss largely improve the performance of FER with occlusion and variant
pose. Our method also achieves state-of-the-art results on FERPlus, AffectNet,
RAF-DB, and SFEW. Code and the collected test data will be publicly available.

该论文提出了一种基于面部区域注意力网络（RAN）和面部动作单元定义的区域偏差损失，解决面部表情识别中的遮挡和姿态变化问题，并在多个数据集上验证其实验效果。

面部表情识别的区域注意力网络：针对姿态和遮挡的鲁棒性

Region Attention Networks for Pose and Occlusion Robust Facial  Expression Recognition

Re-identifying a person across multiple disjoint camera views is important
for intelligent video surveillance, smart retailing and many other
applications. However, existing person re-identification (ReID) methods are
challenged by the ubiquitous occlusion over persons and suffer from performance
degradation. This paper proposes a novel occlusion-robust and alignment-free
model for occluded person ReID and extends its application to realistic and
crowded scenarios. The proposed model first leverages the full convolution
network (FCN) and pyramid pooling to extract spatial pyramid features. Then an
alignment-free matching approach, namely Foreground-aware Pyramid
Reconstruction (FPR), is developed to accurately compute matching scores
between occluded persons, despite their different scales and sizes. FPR uses
the error from robust reconstruction over spatial pyramid features to measure
similarities between two persons. More importantly, we design an
occlusion-sensitive foreground probability generator that focuses more on clean
human body parts to refine the similarity computation with less contamination
from occlusion. The FPR is easily embedded into any end-to-end person ReID
models. The effectiveness of the proposed method is clearly demonstrated by the
experimental results (Rank-1 accuracy) on three occluded person datasets:
Partial REID (78.30\%), Partial iLIDS (68.08\%) and Occluded REID (81.00\%);
and three benchmark person datasets: Market1501 (95.42\%), DukeMTMC (88.64\%)
and CUHK03 (76.08\%)

本文提出了一种抵御遮挡和无需精确定位的人物重识别方法，并扩展其应用于更为真实和拥挤的场景中，在三个挑战性遮挡数据集和三个基准人物数据集上获得了极高的准确率。