Depictions of similar human body configurations can vary with changing
viewpoints. Using only 2D information, we would like to enable vision
algorithms to recognize similarity in human body poses across multiple views.
This ability is useful for analyzing body movements and human behaviors in
images and videos. In this paper, we propose an approach for learning a compact
view-invariant embedding space from 2D joint keypoints alone, without
explicitly predicting 3D poses. Since 2D poses are projected from 3D space,
they have an inherent ambiguity, which is difficult to represent through a
deterministic mapping. Hence, we use probabilistic embeddings to model this
input uncertainty. Experimental results show that our embedding model achieves
higher accuracy when retrieving similar poses across different camera views, in
comparison with 2D-to-3D pose lifting models. We also demonstrate the
effectiveness of applying our embeddings to view-invariant action recognition
and video alignment. Our code is available at
this https URL

本文使用 2Djoint keypoints 学习压缩的视角不变嵌入空间，通过建立概率嵌入模型，实现对不同视角人体姿态的相似性识别，相关技术可应用于姿态识别和视频对齐等方面。

基于概率的视角不变人体姿态嵌入

View-Invariant Probabilistic Embedding for Human Pose

Despite the significant improvement in the performance of monocular pose
estimation approaches and their ability to generalize to unseen environments,
multi-view (MV) approaches are often lagging behind in terms of accuracy and
are specific to certain datasets. This is mainly due to the fact that (1)
contrary to real world single-view (SV) datasets, MV datasets are often
captured in controlled environments to collect precise 3D annotations, which do
not cover all real world challenges, and (2) the model parameters are learned
for specific camera setups. To alleviate these problems, we propose a two-stage
approach to detect and estimate 3D human poses, which separates SV pose
detection from MV 3D pose estimation. This separation enables us to utilize
each dataset for the right task, i.e. SV datasets for constructing robust pose
detection models and MV datasets for constructing precise MV 3D regression
models. In addition, our 3D regression approach only requires 3D pose data and
its projections to the views for building the model, hence removing the need
for collecting annotated data from the test setup. Our approach can therefore
be easily generalized to a new environment by simply projecting 3D poses into
2D during training according to the camera setup used at test time. As 2D poses
are collected at test time using a SV pose detector, which might generate
inaccurate detections, we model its characteristics and incorporate this
information during training. We demonstrate that incorporating the detector's
characteristics is important to build a robust 3D regression model and that the
resulting regression model generalizes well to new MV environments. Our
evaluation results show that our approach achieves competitive results on the
Human3.6M dataset and significantly improves results on a MV clinical dataset
that is the first MV dataset generated from live surgery recordings.

提出使用多视角和单视角数据训练的两阶段方法来检测和估计 3D 人体姿势，通过将数据进行正确的分类来构建准确的模型，避免了数据采集难题。该方法在 Human3.6M 数据集上获得了竞争性的结果，并在 MV 临床数据集上显著提高了结果。