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.

Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped with multi-resolution supervision for training and multiresolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize keypoints more precisely, especially for small person. The feature pyramid in HigherHRNet consists of feature map outputs from HRNet and upsampled higher-resolution outputs through a transposed convolution. HigherHR-Net outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves new state-of-the-art result on COCO test-dev (70.5% AP) without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. HigherHRNet even surpasses all topdown methods on CrowdPose test (67.6% AP), suggesting its robustness in crowded scene. The code and models are available at https://github.com/HRNet/ Higher-HRNet-Human-Pose-Estimation.

There has been significant progress on pose estimation and increasing interests on pose tracking in recent years. At the same time, the overall algorithm and system complexity increases as well, making the algorithm analysis and comparison more difficult. This work provides simple and effective baseline methods. They are helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. The code will be available at https://github. com/leoxiaobin/pose.pytorch.

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.

Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process. However, we found that some human anatomical keypoints kept their topology invariance, which can help to localize them more accurately when detecting the keypoints on the feature map. But to the best of our knowledge, there is no literature that has specifically studied it. Thus, in this paper, we present a novel 2D human pose estimation method with explicit anatomical keypoints structure constraints, which introduces the topology constraint term that consisting of the differences between the distance and direction of the keypoint-to-keypoint and their groundtruth in the loss object. More importantly, our proposed model can be plugged in the most existing bottom-up or top-down human pose estimation methods and improve their performance. The extensive experiments on the benchmark dataset: COCO keypoint dataset, show that our methods perform favorably against the most existing bottom-up and top-down human pose estimation methods, especially for Lite-HRNet, when our model is plugged into it, its AP scores separately raise by 2.9\% and 3.3\% on COCO val2017 and test-dev2017 datasets.

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.

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。

人类的姿势估计旨在弄清不同场景中所有人的关键。尽管结果有希望，但目前的方法仍然面临一些挑战。现有的自上而下的方法单独处理一个人，而没有不同的人与所在的场景之间的相互作用。因此，当发生严重闭塞时，人类检测的表现会降低。另一方面，现有的自下而上方法同时考虑所有人，并捕获整个图像的全局知识。但是，由于尺度变化，它们的准确性不如自上而下的方法。为了解决这些问题，我们通过整合自上而下和自下而上的管道来探索不同接受场的视觉线索并实现其互补性，提出了一种新颖的双皮线整合变压器（DPIT）。具体而言，DPIT由两个分支组成，自下而上的分支介绍了整个图像以捕获全局视觉信息，而自上而下的分支则从单人类边界框中提取本地视觉的特征表示。然后，从自下而上和自上而下的分支中提取的特征表示形式被馈入变压器编码器，以交互融合全局和本地知识。此外，我们定义了关键点查询，以探索全景和单人类姿势视觉线索，以实现两个管道的相互互补性。据我们所知，这是将自下而上和自上而下管道与变压器与人类姿势估计的变压器相结合的最早作品之一。关于可可和MPII数据集的广泛实验表明，我们的DPIT与最先进的方法相当。

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。

In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named Corner-Net. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, which outperforms all existing one-stage detectors by at least 4.9%. Meanwhile, with a faster inference speed, CenterNet demonstrates quite comparable performance to the top-ranked two-stage detectors. Code is available at https://github.com/ Duankaiwen/CenterNet.

We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that Corner-Net achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representations, in part because they are compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A topdown architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using FPN in a basic Faster R-CNN system, our method achieves state-of-the-art singlemodel results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 6 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available.

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.

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/

我们提出了一种直接的，基于回归的方法，以从单个图像中估计2D人姿势。我们将问题提出为序列预测任务，我们使用变压器网络解决了问题。该网络直接学习了从图像到关键点坐标的回归映射，而无需诉诸中间表示（例如热图）。这种方法避免了与基于热图的方法相关的许多复杂性。为了克服以前基于回归的方法的特征错位问题，我们提出了一种注意机制，该机制适应与目标关键最相关的功能，从而大大提高了准确性。重要的是，我们的框架是端到端的可区分，并且自然学会利用关键点之间的依赖关系。两个主要的姿势估计数据集在MS-Coco和MPII上进行的实验表明，我们的方法在基于回归的姿势估计中的最新方法显着改善。更值得注意的是，与最佳的基于热图的姿势估计方法相比，我们的第一种基于回归的方法是有利的。

我们观察到，由于不同身体部位的生物学约束，人类的姿势表现出强大的群体结构相关性和空间耦合。可以探索这种群体结构相关性，以提高人类姿势估计的准确性和鲁棒性。在这项工作中，我们开发了一个自我控制的预测验证网络，以表征和学习训练过程中关键点之间的结构相关性。在推理阶段，来自验证网络的反馈信息使我们能够进一步优化姿势预测，从而显着提高了人类姿势估计的性能。具体而言，我们根据人体的生物结构将关键点分组分组。在每个组中，关键点进一步分为两个子集，高信心基础关键点和低信心终端关键点。我们开发一个自我约束的预测验证网络，以在这些关键点子集之间执行前向和向后的预测。姿势估计以及通用预测任务中的一个基本挑战是，由于无法获得地面真相，因此我们没有机制可以验证获得的姿势估计或预测结果是否准确。一旦成功学习，验证网络将用作前向姿势预测的准确性验证模块。在推理阶段，它可用于指导低保持信心关键点的姿势估计结果的局部优化，而高信心关键点的自我约束损失是目标函数。我们对基准MS可可和人群数据集的广泛实验结果表明，所提出的方法可以显着改善姿势估计结果。

本文介绍了Houghnet，这是一种单阶段，无锚，基于投票的，自下而上的对象检测方法。受到广义的霍夫变换的启发，霍尼特通过在该位置投票的总和确定了某个位置的物体的存在。投票是根据对数极极投票领域的近距离和长距离地点收集的。由于这种投票机制，Houghnet能够整合近距离和远程的班级条件证据以进行视觉识别，从而概括和增强当前的对象检测方法，这通常仅依赖于本地证据。在可可数据集中，Houghnet的最佳型号达到$ 46.4 $ $ $ ap $（和$ 65.1 $ $ $ ap_ {50} $），与自下而上的对象检测中的最先进的作品相同，超越了最重要的一项 - 阶段和两阶段方法。我们进一步验证了提案在其他视觉检测任务中的有效性，即视频对象检测，实例分割，3D对象检测和人为姿势估计的关键点检测以及其他“图像”图像生成任务的附加“标签”，其中集成的集成在所有情况下，我们的投票模块始终提高性能。代码可在https://github.com/nerminsamet/houghnet上找到。

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。

We propose a sparse end-to-end multi-person pose regression framework, termed QueryPose, which can directly predict multi-person keypoint sequences from the input image. The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization. However, the dense paradigm introduces complex and redundant post-processes during inference. In our framework, each human instance is encoded by several learnable spatial-aware part-level queries associated with an instance-level query. First, we propose the Spatial Part Embedding Generation Module (SPEGM) that considers the local spatial attention mechanism to generate several spatial-sensitive part embeddings, which contain spatial details and structural information for enhancing the part-level queries. Second, we introduce the Selective Iteration Module (SIM) to adaptively update the sparse part-level queries via the generated spatial-sensitive part embeddings stage-by-stage. Based on the two proposed modules, the part-level queries are able to fully encode the spatial details and structural information for precise keypoint regression. With the bipartite matching, QueryPose avoids the hand-designed post-processes and surpasses the existing dense end-to-end methods with 73.6 AP on MS COCO mini-val set and 72.7 AP on CrowdPose test set. Code is available at https://github.com/buptxyb666/QueryPose.

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.