# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Single-Person Pose Estimation
#### 2.1.2 Heatmap-Based Pose Estimation  
Heatmap-based methods have emerged as the dominant paradigm in two-dimensional (2D) human pose estimation. [PERSON] et al. [24] proposed a pioneering framework that jointly trains a deep convolutional neural network (ConvNet) with a Markov Random Field (MRF)-based graphical model. In this framework, the ConvNet extracts local image features and produces heatmap representations for body parts, while the MRF models spatial dependencies among joints. The joint optimization of these two components significantly enhances pose estimation performance, particularly in scenarios with complex joint interactions and occlusion. This work was among the first to integrate convolutional neural networks with heatmap-based representations and introduced a multi-scale feature fusion scheme that laid the foundation for numerous subsequent studies. [PERSON] et al. [25] introduced Convolutional Pose Machines (CPM), a cascaded architecture that progressively refines heatmap predictions through multiple processing stages. A key innovation of CPM is the use of intermediate supervision at each stage, which facilitates gradient flow and guides the learning process. By integrating convolutional neural networks with a pose machine framework, the CPM implicitly captures long-range dependencies via stage-wise refinement. This multi-stage design, together with intermediate supervision, markedly enhances the accuracy and robustness of human pose estimation, particularly in challenging scenes with complex poses and occlusion. [PERSON] et al. [26] introduced the Stacked Hourglass convolutional network, a ground-breaking architecture for human pose estimation. This architecture captures features at multiple scales while preserving spatial information at each resolution via skip connections. By stacking multiple hourglass modules in an end-to-end fashion, the network performs repeated bottom-up and top-down inference, enabling the iterative refinement of predictions. Intermediate supervision is applied at each stage to facilitate optimization. This architecture effectively models spatial relationships among body joints and has become a foundational benchmark for numerous subsequent studies in 2D human pose estimation. To address the challenge of scale variations across different human body parts, [PERSON] et al. [27] proposed the Pyramid Residual Module (PRM). This module enables deep convolutional neural networks (DCNNs) to extract multi-scale features, thereby improving robustness to scale variations in human pose estimation. [PERSON] et al. [28] proposed an end-to-end human pose estimation framework that integrates multi-context attention mechanisms with enhanced residual units (HRUs). The model generates attention maps enriched with multi-resolution and multi-level semantic representations using a stacked hourglass network. To refine the attention maps, a Conditional Random Field (CRF) is employed to capture spatial dependencies among adjacent keypoints. This work was among the first to introduce attention mechanisms into heatmap-based pose estimation, thereby improving spatial attention precision and enhancing joint localization accuracy. This approach provides an efficient and accurate solution for human pose estimation tasks. [PERSON] et al. [11] proposed a simple yet effective architecture that combines a ResNet backbone with sequential upsampling and convolutional layers. This design significantly reduces architectural complexity while maintaining strong performance, thereby facilitating easier analysis and comparison across methods. Despite its simplicity, the deconvolution-based head, coupled with the ResNet backbone, achieves performance comparable to that of more complex contemporary models. This work demonstrated that a straightforward architecture can be both effective and practical, thereby promoting accessibility and real-world applicability in human pose estimation research.