# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Multi-Person Pose Estimation
#### 2.2.1 Top-Down Approach of 2D Multi-Person Pose Estimation  
[PERSON] et al. [10] proposed Mask R-CNN, an extension of the Faster R-CNN framework developed for efficient object detection and instance segmentation. In addition to the classification and bounding box regression branches in Faster R-CNN, Mask R-CNN introduces a parallel branch that predicts a pixel-level segmentation mask for each Region of Interest (RoI). This design allows the model to perform accurate instance segmentation while preserving object classification and localization capabilities. A key innovation is the replacement of RoIPool with RoIAlign, which eliminates quantization errors and ensures precise alignment between extracted features and the original image at the pixel level. This substantially improves segmentation quality. Despite its additional functionality, Mask R-CNN remains computationally efficient, introducing only a marginal increase in inference  
Figure 4: Approaches to 2D multi-person pose estimation: (**a**) top-down and (**b**) bottom-up. time compared to Faster R-CNN. It is also straightforward to train and generalize. By integrating keypoint prediction into a general object detection framework, Mask R-CNN has become a standard top-down baseline for multi-person pose estimation. [PERSON] et al. [9] proposed an efficient multi-person 2D human pose estimation method based on Part Affinity Fields (PAFs). PAFs encode a set of 2D vector fields that capture the position and orientation of limbs by modeling pairwise associations between body keypoints. This representation allows the method to robustly handle complex scenarios such as occlusion, limb overlap, and inter-person interactions. After detecting all keypoints, a greedy graph-based parsing algorithm is applied to associate them based on PAFs, thereby assembling complete poses for multiple individuals. Unlike traditional top-down approaches that rely on person detection, followed by single-person pose estimation, this method avoids errors caused by detection failures and decouples the inference time from the number of people in the scene, leading to higher computational efficiency. The use of PAFs for keypoint grouping is a key innovation that laid the foundation for early integrations of top-down and bottom-up paradigms in multi-person pose estimation. [PERSON] et al. [33] introduced MultiPoseNet, a multi-task framework for efficient multi-person pose estimation. The central innovation of this framework is the Pose Residual Network (PRN), which is designed to accurately assign detected keypoints to their corresponding human instances. PRN utilizes a residual multilayer perceptron that jointly considers the configuration of all keypoints, enabling keypoint-to-person association to be performed in a single forward pass. This design significantly improves association accuracy, especially in challenging scenarios with substantial person overlap. To address the heavy model dependency and architectural complexity often associated with human pose estimation, [PERSON] et al. [23] introduced PoseFix, a model-agnostic pose refinement network. Unlike conventional end-to-end approaches that tightly couple pose estimation with specific architectures, PoseFix decouples the refinement stage and operates as an independent post-processing module. It can be flexibly integrated into various top-down frameworks to enhance keypoint localization accuracy, regardless of the underlying backbone architecture. [PERSON] et al. [34] introduced the Multi-Instance Pose Network (MIPNet) for robust multi-person pose estimation. A core component of MIPNet is the Multi-Instance Modulation Block (MIMB), which adaptively modulates channel-wise features for individual instances. By enabling instance-specific feature transformation, MIMB enhances the network's ability to differentiate and predict multiple human poses simultaneously. This design significantly improves performance in crowded and occluded scenes, where conventional methods often fail. [PERSON] et al.