# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 4 Top-Performing Models on Benchmark Datasets
### Top-Performing Models on the LSP Dataset  
On the LSP dataset, OmniPose achieved a PCK accuracy of 99.5%, approaching the upper bound of performance on this single-person pose benchmark [61]. OmniPose is based on a single-stage, end-to-end trainable framework that integrates an optimized HRNet backbone with the novel WASPv2 module. The input image is first processed by an enhanced HRNet, which extracts high-resolution, multi-scale feature representations. These features are then passed to the WASPv2 module for multi-scale fusion and decoding, generating Gaussian heatmaps for each joint to enable precise localization. To enhance localization accuracy, the HRNet backbone is refined by replacing conventional upsampling with a Gaussian-modulated deconvolution operation. This modification reduces pixel-level quantization errors and improves the precision of local keypoint regression. OmniPose adopts a unified end-to-end training framework that eliminates the need for complex post-processing steps. The model is supervised with a Gaussian heatmap regression loss that facilitates accurate keypoint localization. To improve robustness and generalization, data augmentation techniques such as rotation, scaling, and flipping are applied during training. Additionally, the network supports multi-resolution training, allowing a flexible balance between accuracy and inference efficiency. Figure 12 illustrates the framework of OmniPose for multi-person pose estimation. The core innovations of OmniPose reside in the design of the WASPv2 module and the Gaussian-modulated deconvolution mechanism. WASPv2 utilizes a hierarchical stack of dilated convolutions to significantly expand the receptive field and improve contex \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Year** & **Backbone** & **AP** & **Input Size** & **GFLOPs** & **Params** & **Characteristic** \\ \hline ViTPose [35] & 2022 & ViTAE-G & 80.9 & 576 \(\times\) 432 & \(>\)200 & 1 B & \
\begin{tabular}{c} ViTPose is built on a streamlined ViT architecture, offering a compelling combination of high performance, scalability, and strong transferability. \\ \end{tabular} \\ \hline UDP-Pose-PSA [59] & 2021 & HRNet-W48 & 79.5 & 384 \(\times\) 288 & 35.4 & 70.1 M &
\begin{tabular}{c} UDP-Pose-PSA integrates HRNet-W48 with polarized self-attention to enhance long-range dependency modeling, thereby improving the accuracy and fine-grained performance of keypoint detection. \\ \end{tabular} \\ \hline
4 \(\times\) RSN-50 (ensemble) & 2020 & ResNet-50 & 79.2 & 256 \(\times\) 192 & 29.3 & 111.8 M & \begin{tabular}{c} 4 \(\times\) RSN-50 (ensemble) integrates four multi-branch \\ Residual Steps Networks to effectively fuse \\ intra-layer features. \\ \end{tabular} \\ \hline CCM+ [60] & 2020 & HRNet-w48 & 78.9 & 384 \(\times\) 288 & 36.3 & 63.7 M & \
\begin{tabular}{c} CCM+ significantly improves keypoint detection \\ accuracy through cascaded contextual fusion, \\ sub-pixel localization, and an efficient \\ training strategy. \\ \end{tabular} \\ \hline UDP-Pose-PSA [59] & 2021 & HRNet-W48 & 78.9 & 256 \(\times\) 192 & 15.7 & 70.1 M &
\begin{tabular}{c} UDP-Pose-PSA achieves an AP of 78.9 at an input \\ resolution of 256 \(\times\) 192, which is slightly lower than \\ the 79.5 obtained at 384 \(\times\) 288. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance of top-ranked models (2 nd–6 th) on the COCO test-dev dataset. Figure 12: OmniPose framework for multi-person pose estimation. The input image is processed using an enhanced HRNet and the WASPv2 module to generate heatmaps for each joint [61]. tual feature representation. It directly generates keypoint heatmaps, eliminating the need for a separate decoding module and enhancing the overall computational efficiency. Simultaneously, the Gaussian-modulated deconvolution replaces conventional upsampling, effectively reducing pixel quantization errors and enhancing keypoint localization accuracy.