Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.

We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.

在多人2D姿势估计中，自下而上的方法同时预测了所有人的姿势，与自上而下的方法不同，不依赖于人类的检测。但是，与现有的自上而下方法相比，SOTA自下而上的方法的精度仍然不如较低。这是由于预测的人类姿势是根据不一致的人类边界箱中心进行回归的，并且缺乏人类规范的正常化，从而导致预测的人类姿势被遗漏了不准确和小规模的人。为了推动自下而上的姿势估计的信封，我们首先提出了多尺度训练，以增强网络以通过单尺度测试来处理规模变化，尤其是对于小规模的人。其次，我们介绍了双解剖中心（即头部和身体），在这里我们可以更准确，可靠地预测人类的姿势，尤其是对于小规模的人。此外，现有的自下而上方法采用多尺度测试来以多个额外的前向通行证的价格提高姿势估计的准确性，这削弱了自下而上方法的效率，与自上而下的方法相比，核心强度。相比之下，我们的多尺度训练使该模型能够预测单个前向通行证（即单尺度测试）中的高质量姿势。我们的方法在边界框的精度方面取得了38.4 \％的改进，在边界框上进行了39.1 \％的改进，以对可可的具有挑战性的小规模人群进行对现状（SOTA）的回忆（SOTA）。对于人类姿势AP评估，我们在带有单尺度测试的可可测试-DEV集中实现了新的SOTA（71.0 AP）。我们还在跨数据库评估中在Ochuman数据集上实现了最高的性能（40.3 AP）。

在诸如人类姿态估计的关键点估计任务中，尽管具有显着缺点，但基于热线的回归是主要的方法：Heatmaps本质上遭受量化误差，并且需要过多的计算来产生和后处理。有动力寻找更有效的解决方案，我们提出了一种新的热映射无关声点估计方法，其中各个关键点和空间相关的关键点（即，姿势）被建模为基于密集的单级锚的检测框架内的对象。因此，我们将我们的方法Kapao（发音为“KA-Pow！”）对于关键点并作为对象构成。我们通过同时检测人姿势对象和关键点对象并融合检测来利用两个对象表示的强度来将Kapao应用于单阶段多人人类姿势估算问题。在实验中，我们观察到Kapao明显比以前的方法更快，更准确，这极大地来自热爱处理后处理。此外，在不使用测试时间增强时，精度速度折衷特别有利。我们的大型型号Kapao-L在Microsoft Coco Keypoints验证集上实现了70.6的AP，而无需测试时增强，其比下一个最佳单级模型更准确，4.0 AP更准确。此外，Kapao在重闭塞的存在下擅长。在繁荣试验套上，Kapao-L为一个单级方法实现新的最先进的准确性，AP为68.9。

我们提出Bapose，一种新颖的自下而上的方法，实现了多人姿态估计的最先进结果。我们的最终培训框架利用了解开的多尺度瀑布架构，并将自适应卷曲融合在拥挤的场景中更准确地推断出闭塞的关键点。由BAPOSE中的解开瀑布模块获得的多尺度表示，利用级联架构中进行逐行滤波的效率，同时保持与空间金字塔配置的多尺度视图相当。我们对挑战性的Coco和Crowdose数据集的结果表明，Bapose是多人姿态估计的高效且稳健的框架，实现了最先进的准确性的显着改善。

我们研究了在紧邻人类机器人相互作用的背景下，最先进的人关键点探测器的性能。在这种情况下的检测是具体的，因为只有手和躯干等身体部位的子集在视野中。特别是（i）我们从近距离图像的角度调查了具有人类姿势注释的现有数据集，并准备并使公开可用的新人（HICP）数据集； （ii）我们在此数据集上进行定量和定性比较人类全身2D关键点检测方法（openpose，mmpose，onphapose，detectron2）； （iii）由于对手指的准确检测对于使用交接的应用至关重要，因此我们评估了介质手工检测器的性能； （iv）我们在头部上带有RGB-D摄像头的人形机器人上部署算法，并在3D Human KeyPoint检测中评估性能。运动捕获系统用作参考。在紧邻近端的最佳性能全身关键点探测器是mmpose和字母，但两者都难以检测手指。因此，我们提出了在单个框架中为人体和手介载体的mmpose或字母组合的组合，提供了最准确，最强大的检测。我们还分析了单个探测器的故障模式 - 例如，图像中人的头部缺失在多大程度上降低了性能。最后，我们在一个场景中演示了框架，其中类人类机器人与人相互作用的人类机器人使用检测到的3D关键点进行全身避免动作。

本文介绍了使用变压器解决关键点检测和实例关联的新方法。对于自下而上的多人姿势估计模型，他们需要检测关键点并在关键点之间学习关联信息。我们认为这些问题可以完全由变压器解决。具体而言，变压器中的自我关注测量任何一对位置之间的依赖性，这可以为关键点分组提供关联信息。但是，天真的注意力模式仍然没有主观控制，因此无法保证关键点始终会参加它们所属的实例。为了解决它，我们提出了一种监督多人关键点检测和实例关联的自我关注的新方法。通过使用实例掩码来监督自我关注的实例感知，我们可以基于成对引人注定分数为其对应的实例分配检测到的关键字，而无需使用预定义的偏移量字段或嵌入像基于CNN的自下而上模型。我们方法的另一个好处是可以从监督的注意矩阵直接获得任何数量的人的实例分段结果，从而简化了像素分配管道。对Coco多人关键点检测挑战和人实例分割任务的实验证明了所提出的方法的有效性和简单性，并显示出于针对特定目的控制自我关注行为的有希望的方法。

本文调查了2D全身人类姿势估计的任务，该任务旨在将整个人体（包括身体，脚，脸部和手）局部定位在整个人体上。我们提出了一种称为Zoomnet的单网络方法，以考虑到完整人体的层次结构，并解决不同身体部位的规模变化。我们进一步提出了一个称为Zoomnas的神经体系结构搜索框架，以促进全身姿势估计的准确性和效率。Zoomnas共同搜索模型体系结构和不同子模块之间的连接，并自动为搜索的子模块分配计算复杂性。为了训练和评估Zoomnas，我们介绍了第一个大型2D人类全身数据集，即可可叶全体V1.0，它注释了133个用于野外图像的关键点。广泛的实验证明了Zoomnas的有效性和可可叶v1.0的重要性。

我们提出了一种用于多实例姿态估计的端到端培训方法，称为诗人（姿势估计变压器）。将卷积神经网络与变压器编码器 - 解码器架构组合，我们将多个姿势估计从图像标记为直接设置预测问题。我们的模型能够使用双方匹配方案直接出现所有个人的姿势。诗人使用基于集的全局损失进行培训，该丢失包括关键点损耗，可见性损失和载重损失。诗歌的原因与多个检测到的个人与完整图像上下文之间的关系直接预测它们并行姿势。我们展示诗人在Coco Keypoint检测任务上实现了高精度，同时具有比其他自下而上和自上而下的方法更少的参数和更高推理速度。此外，在将诗人应用于动物姿势估计时，我们表现出了成功的转移学习。据我们所知，该模型是第一个端到端的培训多实例姿态估计方法，我们希望它将成为一种简单而有前途的替代方案。

The topic of multi-person pose estimation has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as occluded keypoints, invisible keypoints and complex background, which cannot be well addressed. In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints. More specifically, our algorithm includes two stages: Glob-alNet and RefineNet. GlobalNet is a feature pyramid network which can successfully localize the "simple" keypoints like eyes and hands but may fail to precisely recognize the occluded or invisible keypoints. Our RefineNet tries explicitly handling the "hard" keypoints by integrating all levels of feature representations from the Global-Net together with an online hard keypoint mining loss. In general, to address the multi-person pose estimation problem, a top-down pipeline is adopted to first generate a set of human bounding boxes based on a detector, followed by our CPN for keypoint localization in each human bounding box. Based on the proposed algorithm, we achieve stateof-art results on the COCO keypoint benchmark, with average precision at 73.0 on the COCO test-dev dataset and 72.1 on the COCO test-challenge dataset, which is a 19% relative improvement compared with 60.5 from the COCO 2016 keypoint challenge. Code 1 and the detection results are publicly available for further research.

大多数实时人类姿势估计方法都基于检测接头位置。使用检测到的关节位置，可以计算偏差和肢体的俯仰。然而，由于这种旋转轴仍然不观察，因此不能计算沿着肢体沿着肢体至关重要的曲折，这对于诸如体育分析和计算机动画至关重要。在本文中，我们引入了方向关键点，一种用于估计骨骼关节的全位置和旋转的新方法，仅使用单帧RGB图像。灵感来自Motion-Capture Systems如何使用一组点标记来估计全骨骼旋转，我们的方法使用虚拟标记来生成足够的信息，以便准确地推断使用简单的后处理。旋转预测改善了接头角度最佳报告的平均误差48％，并且在15个骨骼旋转中实现了93％的精度。该方法还通过MPJPE在原理数据集上测量，通过MPJPE测量，该方法还改善了当前的最新结果14％，并概括为野外数据集。

In this paper, we are interested in the human pose estimation problem with a focus on learning reliable highresolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process.We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutliresolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich highresolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. In addition, we show the superiority of our network in pose tracking on the PoseTrack dataset. The code and models have been publicly available at https://github.com/leoxiaobin/ deep-high-resolution-net.pytorch.

Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research. Nevertheless, assuming a multiple-view system composed of several regular RGB cameras, 3D multi-pose estimation presents several challenges. First of all, each person must be uniquely identified in the different views to separate the 2D information provided by the cameras. Secondly, the 3D pose estimation process from the multi-view 2D information of each person must be robust against noise and potential occlusions in the scenario. In this work, we address these two challenges with the help of deep learning. Specifically, we present a model based on Graph Neural Networks capable of predicting the cross-view correspondence of the people in the scenario along with a Multilayer Perceptron that takes the 2D points to yield the 3D poses of each person. These two models are trained in a self-supervised manner, thus avoiding the need for large datasets with 3D annotations.

本文提出了一个统一的框架到（i）找到球，（ii）预测姿势，（iii）在团队体育场景中分段播放器的实例掩码。这些问题对自动体育分析，生产和广播有高兴趣。常见做法是通过利用通用最先进的模型，例如Panoptic-Deeblab来单独解决每个问题，用于玩家分割。除了从单任务模型的乘法乘以增加的复杂性之外，由于团队体育场景的复杂性和特异性，使用现成的架子模型也会阻碍性能，如强大的遮挡和运动模糊。为了规避这些限制，我们的论文提出培训一种单一的模型，它通过组合零件强度场和空间嵌入原理来预测球和玩家掩模和姿势。部件强度场提供球和播放器位置，以及播放器接头位置。然后利用空间嵌入来将播放器实例像素联系到其各自的播放器中心，而且还将播放器接头分组成骷髅。我们展示了拟议模型在DeepSport篮球数据集上的有效性，为单独解决每个单独任务的SOA模型实现了可比性的性能。

瑜伽是全球广受好评的，广泛推荐的健康生活实践。在执行瑜伽时保持正确的姿势至关重要。在这项工作中，我们采用了从人类姿势估计模型中的转移学习来提取整个人体的136个关键点，以训练一个随机的森林分类器，该分类器用于估算瑜伽室。在内部收集的内部收集的瑜伽视频数据库中评估了结果，该数据库是从4个不同的相机角度记录的51个主题。我们提出了一个三步方案，用于通过对1）看不见的帧，2）看不见的受试者进行测试来评估瑜伽分类器的普遍性。我们认为，对于大多数应用程序，对看不见的主题的验证精度和看不见的摄像头是最重要的。我们经验分析了三个公共数据集，转移学习的优势以及目标泄漏的可能性。我们进一步证明，分类精度在很大程度上取决于所采用的交叉验证方法，并且通常会产生误导。为了促进进一步的研究，我们已公开提供关键点数据集和代码。

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.

物体检测通常需要在现代深度学习方法中基于传统或锚盒的滑动窗口分类器。但是，这些方法中的任何一个都需要框中的繁琐配置。在本文中，我们提供了一种新的透视图，其中检测对象被激励为高电平语义特征检测任务。与边缘，角落，斑点和其他特征探测器一样，所提出的探测器扫描到全部图像的特征点，卷积自然适合该特征点。但是，与这些传统的低级功能不同，所提出的探测器用于更高级别的抽象，即我们正在寻找有物体的中心点，而现代深层模型已经能够具有如此高级别的语义抽象。除了Blob检测之外，我们还预测了中心点的尺度，这也是直接的卷积。因此，在本文中，通过卷积简化了行人和面部检测作为直接的中心和规模预测任务。这样，所提出的方法享有一个无盒设置。虽然结构简单，但它对几个具有挑战性的基准呈现竞争准确性，包括行人检测和面部检测。此外，执行交叉数据集评估，证明所提出的方法的卓越泛化能力。可以访问代码和模型（https://github.com/liuwei16/csp和https://github.com/hasanirtiza/pedestron）。

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [11] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster -50 fps on a Titan X (Pascal) GPU -and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [28,29] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm.For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent 26] when they are all used without postprocessing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.

Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve 76.7 mAP on the MPII (multi person) dataset [3]. Our model and source codes are made publicly available. † . * part of this work was done when Hao-Shu Fang was an student intern in Tencent § corresponding author is Cewu Lu † https://cvsjtu.wordpress.com/rmpe-regional-multi-person-pose-estimation/