# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Multi-Person Pose Estimation
#### 2.2.2 Bottom-Up Approach of 2D Multi-Person Pose Estimation  
Bottom-up methods originated from graphical model formulations, where spatial relationships between keypoints were optimized using graph-based structures. With the advancement of deep learning, these approaches have evolved into end-to-end frameworks centered on Part Affinity Fields (PAFs) and heatmaps for simultaneous keypoint detection and association. Since the introduction of OpenPose in 2017--the first framework to achieve efficient multi-person keypoint detection [9]--subsequent research has advanced along three primary dimensions: detection accuracy, inference speed, and system scalability. [PERSON] et al. [9] first introduced Part Affinity Fields (PAFs), which model the spatial connections between body parts as two-dimensional vector fields. This formulation established the core architecture of bottom-up pose estimation methods. PAFs effectively addressed the challenge of assembling individual keypoints into complete human skeletons, thereby enabling multi-person pose estimation without the need for explicit person detection. This pioneering work led to the development of the OpenPose framework, which has since become a widely adopted benchmark in the field. In follow-up work, [PERSON] et al. [36] extended OpenPose to support additional skeletal models and enhanced the keypoint decoding strategy. To address the keypoint association errors in crowded scenes observed in OpenPose, [PERSON] et al. [37] introduced PersonLab. PersonLab is a box-free, bottom-up framework for multi-person pose estimation and instance segmentation. Built on a fully convolutional network, PersonLab performs keypoint detection and instance grouping simultaneously in a single forward pass. The framework introduces geometric embeddings for each keypoint, enabling efficient and accurate instance association without relying on bounding boxes. This approach improves the reliability of keypoint grouping, especially in densely populated and highly occluded scenes. [PERSON] et al. [38] introduced a bounding-box-constrained strategy to enhance keypoint grouping accuracy in bottom-up pose estimation. The proposed method employs a multi-stage residual network that jointly predicts keypoint heatmaps and directional fields, enabling full-image pose estimation in a single forward pass. During the association stage, bounding boxes are used to constrain the connection range, reducing the likelihood of incorrect keypoint associations between individuals. In addition, non-maximum suppression and broken-link recovery techniques are incorporated to further improve overall accuracy and robustness. [PERSON] et al. [39] conducted the first systematic comparison of bottom-up and top-down approaches using the PoseTrack dataset. The study revealed that bottom-up methods exhibit superior generalization performance in crowded and occluded scenarios. Based on this observation, they proposed a hybrid strategy that combines the strengths of both paradigms to improve overall pose estimation accuracy. [PERSON] et al. [40] revisited bottom-up multi-person pose estimation and proposed a more intuitive and efficient framework. The proposed method focuses on accurately detecting the keypoints of all individuals in an image, resulting in substantial performance improvements. [PERSON] et al. [41] argued that accurate keypoint regression requires learning feature representations that are specifically focused on keypoint regions. Based on this insight, they proposed a novel bottom-up framework named Decoupled Keypoint Regression (DEKR). DEKR employs adaptive convolutions and a multi-branch architecture to decouple the regression of individual keypoints, thereby improving localization precision. [PERSON] et al. [42] proposed PETR, the first end-to-end transformer-based framework for multi-person human pose estimation. PETR formulates pose estimation as a hierarchical set prediction task, effectively eliminating the dependence on handcrafted components.