# 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
To address the limitations of conventional networks that downsample and then upsample feature representations, [PERSON] et al. (2019) proposed the High-Resolution Network (HRNet). Unlike traditional architectures, HRNet maintains high-resolution representations throughout the entire network by connecting multiple parallel convolutional streams that operate at different resolutions. By repeatedly exchanging information across these multi-resolution branches, HRNet effectively enhances both spatial accuracy and semantic representation. This design significantly improves pose estimation performance, particularly in scenarios requiring fine-grained localization. [PERSON] et al. (2019) were the first to systematically investigate the substantial impact of heatmap coordinate decoding on pose estimation performance. Their work addressed a critical gap in the literature by identifying and analyzing design flaws in the coordinate encoding and decoding processes of heatmap-based methods, which had previously received little attention. To address the quantization errors and information loss inherent in conventional decoding strategies, [PERSON] et al. proposed the Distribution-Aware Coordinate Representation of Keypoints (DARK), a novel representation that models the underlying distribution of keypoint locations. DARK requires no modification to existing network architectures and can be seamlessly integrated into mainstream models such as HRNet and SimpleBaseline. Experimental results demonstrate that DARK significantly improves the average precision of keypoint detection. To address data processing bias in human pose estimation, [PERSON] et al. (2019) proposed the Unbiased Data Processing (UDP) strategy. They systematically analyzed two major sources of errors that are commonly observed in mainstream methods: misalignment during image flipping and statistical bias in the coordinate encoding-decoding process. UDP addresses these issues by replacing pixel-based measurements with unit-length scaling in continuous space, thereby enabling the precise alignment of flipped predictions during inference. This enhances the generalization capability of pose estimation models. As a model-agnostic strategy, UDP can be readily integrated into a wide range of state-of-the-art pose estimation frameworks. [PERSON] et al. (2019) introduced a cascaded transformer architecture for regression-based human pose estimation. The model exploits the transformer's encoder-decoder structure to unify person detection with keypoint localization. Unlike heatmap-based approaches, it removes complex post-processing and hand-crafted heuristics, enabling fully end-to-end training and inference. Moreover, leveraging the transformer for keypoint prediction yields interpretable joint-association modeling, offering explicit insight into keypoint relationships. To address the limitations of traditional heatmap-based methods in modeling global structural information, [PERSON] et al. (2019) proposed a novel two-stage human pose estimation framework, termed Graph-PCNN. The framework adopts a coarse-to-fine pipeline: the first stage performs coarse keypoint localization, followed by a refinement stage that improves prediction precision. A core component of the framework is the Graph Pose Refinement (GPR) module, which models structural relationships among keypoints via graph-based message passing and feature aggregation. By leveraging these dependencies, GPR effectively refines coarse predictions from the initial stage, resulting in significantly improved keypoint localization accuracy.