# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 4 Top-Performing Models on Benchmark Datasets
### Top-Performing Models on the MPII Dataset  
On the MPII dataset, the Pose Contrastive Transformer (PCT) achieved a state-of-the-art accuracy of 94.3% under the [EMAIL_ADDRESS] metric, representing the highest performance reported on this benchmark to date [53]. The PCT employs a two-stage network architecture. In the encoding stage, a learnable encoder transforms the input pose into a set of tokens, each encoding a semantically meaningful substructure of the human body. These tokens are discretized using a shared codebook, generating class indices corresponding to distinct structural components. In the prediction stage, a classifier predicts the class index of each token based on features extracted from the input image. These predicted indices are  
Figure 9: Example images from the CrowdPose Dataset. subsequently decoded by a pretrained decoder to reconstruct the final human keypoint coordinates. As illustrated in Figure 10, the PCT adopts a two-stage pipeline. The training process similarly comprises two stages. In the first stage, the encoder and codebook are jointly optimized to minimize the reconstruction error of the quantized pose representation, ensuring that each token effectively captures the structural characteristics of its corresponding body part. In the second stage, the classifier is trained to predict token class indices directly from the input image. A pretrained decoder then translates the predicted indices into final keypoint coordinates. This two-stage training paradigm enables the model to learn structured and semantically coherent pose representations, thereby enhancing accuracy and robustness, particularly in complex scenes with severe occlusions. The core innovation of PCT lies in decomposing the holistic human pose into a set of structured and interpretable subcomponents, each encoded as a discrete token. Unlike conventional methods that predict each joint independently, PCT explicitly models inter-joint dependencies through a compositional representation. The use of a shared codebook enables a compact and discrete representation of human poses, thereby reducing representational complexity while preserving rich structural semantics. This design enhances the model's capacity to reason about complex joint relationships and improves robustness under occlusion. Furthermore, the encoder, codebook, and decoder are trained in an end-to-end manner, enabling the unified learning of structured pose representations. As shown in Table 3, the performance of the models on the MPII dataset is compared. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Year** & **Backbone** & **[EMAIL_ADDRESS]** & **Input Size** & **GFLOPs** & **Params** & \multicolumn{2}{c}{**Characteristic**} \\ \hline \hline \multirow{3}{*}{PCT [54]} & \multirow{3}{*}{2023} & \multirow{3}{*}{swin-base} & \multirow{3}{*}{93.8\%} & \multirow{3}{*}{256 \(\times\) 256} & \multirow{3}{*}{15.2} & \multirow{3}{*}{-88 M} & \multicolumn{3}{c}{PCT enhances dependency modeling and occlusion-robust inference by decomposing human poses into structured, discrete symbolic subcomponents.}