# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 2 Two-Dimensional Human Pose Estimation
### Two-Dimensional Single-Person Pose Estimation
#### 2.1.1 Regression-Based Pose Estimation  
Single-person 2D human pose estimation focuses on localizing body joints in images that contain a single subject. Traditional approaches have primarily relied on hand-crafted features and shallow architectures, which have limited their representational capacity. A seminal work by [PERSON] et al. [20] introduced deep neural networks (DNNs) into pose estimation by formulating the task as direct regression to joint coordinates. The DeepPose framework is illustrated in Figure 3.  
This pioneering approach introduced a cascaded regression framework that not only improved estimation accuracy but also effectively incorporated contextual information. It marked the first successful application of deep learning to human pose estimation and has since been widely adopted and extended, establishing itself as a foundational paradigm in the field.  
[PERSON] et al. [21] proposed Iterative Error Feedback (IEF), a novel approach designed to improve the accuracy of human keypoint localization in complex scenarios. Compared to DeepPose, IEF incorporates several key enhancements. First, IEF employs a feedback mechanism that iteratively refines initial predictions, rather than estimating all keypoints in a single forward pass. This iterative correction process enhances overall localization accuracy. Second, it introduces bounded correction steps, allowing the model to make moderate and stable updates. In addition, IEF more effectively captures structural dependencies within the output space, helping to reduce the complexity of direct coordinate regression. Finally, the introduction of the Fixed Path Consolidation (FPC) strategy improves training efficiency by accelerating model convergence.  
[PERSON] et al. [22] proposed a simple yet effective integral operation that bridges and unifies heatmap-based representation with joint coordinate regression. This approach addresses key limitations of conventional heatmap-based methods, including non-differentiable post-processing and quantization errors. By integrating heatmap outputs using a differentiable operation, this method preserves the spatial richness of heatmaps while yielding continuous joint coordinates. As a result, it is considered an advanced variant of regression-based approaches. The fully differentiable nature of the integral operation enables end-to-end optimization. Moreover, the method is applicable to both 2D and 3D pose estimation tasks and supports joint training across them, thereby substantially improving 3D pose estimation performance. The proposed integral regression paradigm introduced a new perspective for human pose estimation and has since been widely adopted and extended in subsequent studies.  
[PERSON] et al. [23] proposed PoseFix, a model-agnostic human pose refinement network designed to improve the accuracy of predictions generated by diverse mainstream pose estimation methods. Unlike conventional refinement strategies that require multi-stage end-to-end training and tight integration with specific architectures, PoseFix operates independently of any specific pose estimation model or its implementation. This design offers  
Figure 3: DeepPose framework: a DNN-based pose regressor and optimizer.  
significant flexibility and ease of use, enabling PoseFix to function as a post-processing module for improving a broad range of pose estimation outputs. Although it was originally developed as a post hoc refinement tool, PoseFix introduced a novel regression-based refinement module that eliminates the need for heatmaps. This heatmap-free regression strategy has inspired follow-up studies in the area of regression-based pose optimization.