# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 4 Top-Performing Models on Benchmark Datasets
### Top-Performing Models on the OCHuman Dataset  
On the OCHuman dataset, UniHCP achieved an average precision (AP) of 87.4, marking the current state-of-the-art performance on this benchmark [64]. UniHCP comprises three key components: a task-agnostic transformer encoder, a task-adaptive decoder, and a task-guided interpreter. The encoder contains 12 transformer layers with a hidden size of 768 and approximately 91.1 million parameters. It extracts general-purpose visual features from the input image. The decoder consists of nine layers with a hidden size of 256 and approximately 14.5 million parameters and is designed to retrieve task-specific features via learned queries. The task-guided interpreter, which comprises approximately 3.5 million parameters, translates the query outputs into task-specific representations. This unified design allows for up to 99.97% parameter sharing across tasks, significantly improving knowledge transfer and enhancing generalization capabilities.  
UniHCP employs a large-scale joint pretraining strategy across multiple tasks. It utilizes 33 human-centric datasets that cover five key areas: pose estimation, semantic part segmentation, pedestrian detection, person re-identification, and human attribute recognition. Through the use of unified task-specific queries and a shared interpreter, the model can simultaneously perform multiple tasks within a unified framework. Figure 13 illustrates the UniHCP framework for human-centric tasks.  
UniHCP is the first model to introduce a unified architecture that efficiently integrates and handles multiple representative human-centric perception tasks. Its query-driven output mechanism replaces conventional task-specific heads, significantly reducing task-specific parameters and enhancing parameter sharing and feature reuse. Furthermore, the model leverages large-scale multi-task joint pretraining to enable extensive cross-task knowledge transfer and feature sharing. Owing to its efficient weight-sharing strategy, UniHCP achieves high parameter and data efficiency, thereby improving its transferability and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Year** & **Backbone** & **PCK** & **Input Size** & **GFLOPs** & **Params** & **Characteristic** \\ \hline Soft-gated Skip & 2020 & Hourglass & 94.8\% & 256 \(\times\) 256 & 9.9 & 8.5 M & \begin{tabular}{c} A channel scaling factor is introduced to optimize \\ feature fusion, enhancing both model accuracy and \\ computational efficiency. \\ \end{tabular} \\ \hline UniPose [56] & 2020 & ResNet-101 & 94.5\% & 1280 \(\times\) 720 & \(\sim\)150 & -48 M & \begin{tabular}{c} UniPose integrates a ResNet backbone with the \\ WASP module to enable efficient end-to-end pose \\ \hline \begin{tabular}{c} Residual \\ Hourglass + ASR \\ + AHO [63] \\ \end{tabular} & 2018 & Stacked & 94.5\% & 256 \(\times\) 256 & \(\sim\)200 & 38 M & \begin{tabular}{c} Residual Hourglass combines residual structures \\ with adversarial enhancement to improve \\ multi-scale feature modeling and the accuracy of \\ keypoint localization. \\ \end{tabular} \\ \hline \begin{tabular}{c} SAHPE- \\ Network [64] \\ \end{tabular} & 2017 & Stacked & 94\% & 256 \(\times\) 256 & -50 & -30 M & \
\begin{tabular}{c} This approach introduces a discriminator to learn \\ structural constraints, thereby enhancing the \\ accuracy and plausibility of pose estimation. \\ \end{tabular} \\ \hline PRMs [27] & 2017 & Stacked & 93.9\% & 256 \(\times\) 256 & 45.9 & 26.9 M &
\begin{tabular}{c} PRMs employs multi-branch convolutions to extract \\ multi-scale features, enhancing the adaptability and \\ accuracy of pose estimation under varying scales. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative performance of top-ranked models (2 nd–6 th) on the LSP dataset.  
adaptability to a wide range of human-centric tasks. As shown in Table 6, the performance of the models on the OCHuman dataset is compared.