# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 4 Top-Performing Models on Benchmark Datasets
### Top-Performing Models on CrowdPose Dataset  
On the CrowdPose dataset, BUCTD achieved an AP score of 78.5, marking the highest reported performance on this benchmark to date [65]. Bottom-Up Conditioned Top-Down (BUCTD) combines the strengths of bottom-up (BU) and top-down (TD) pose estimation paradigms to enhance overall accuracy and robustness. The framework operates in two main stages. In the first stage, a bottom-up model performs initial person detection and pose estimation. The resulting bottom-up predictions are then used as conditional inputs for a top-down refinement network in the second stage. To improve refinement, the top-down network incorporates a Conditional Attention Module (CoAM) that effectively fuses conditional pose information with image features. This design enhances the model's capability to identify and recover occluded or overlapping keypoints, especially in crowded or heavily occluded scenes. BUCTD adopts a staged training pipeline. The bottom-up (BU) model is initially pre-trained to produce reliable pose predictions, which serve as conditional inputs for \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Year** & **Backbone** & **Test AP** & **Input Size** & **GFLOPs** & **Params** & **Characteristic** \\ \hline BUCTD [65] & 2023 & CID-W32 & 47.2 & 256 \(\times\) 192 & -10 & -35 M & \begin{tabular}{c} BUCTD leverages bottom-up pose estimation as a conditional input to enhance accuracy and robustness in crowded and occluded scenarios. \\ \end{tabular} \\ \hline HQNet [66] & 2024 & ViT-L & 45.6 & 512 \(\times\) 512 & -80 & -304 M & \begin{tabular}{c} HQNet enables unified modeling of multi-task human understanding by sharing a transformer decoder and a human query mechanism. \\ \end{tabular} \\ \hline CID [34] & 2022 & HRNet-W48 & 45.0 & 512 \(\times\) 512 & 43.14 & 29.4 M & \begin{tabular}{c} CID introduces a context-instance decoupling mechanism that employs spatial and channel attention to extract instance-aware features for keypoint estimation. \\ \end{tabular} \\ \hline MIPNet [64] & 2021 & HRNet-W48 & 42.5 & 256 \(\times\) 256 & -19 & 28.6 M & \
\begin{tabular}{c} MIPNet introduces a Multi-Instance Modulation Block (MIMB) that enables efficient prediction of multiple pose instances within a single detection box. \\ \end{tabular} \\ \hline HQNet [66] & 2024 & ResNet-50 & 40.0 & 512 \(\times\) 512 & -25 & -53 M &
\begin{tabular}{c} Built on a ResNet-50 backbone, the model offers strong spatial locality modeling, high computational efficiency, and a lightweight structure. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative performance of top-ranked models (2 nd–6 th) on the OCHuman dataset. Figure 13: UniHCP handles diverse human-centric tasks uniformly by employing task-specific queries and a task-guided interpreter. All task predictions are generated in parallel through a unified encoder–decoder transformer framework [64]. training the top-down refinement network. To improve the model's tolerance to prediction noise and enhance robustness, two conditional input sampling strategies are employed during training. The first strategy involves empirical sampling based on actual outputs from the BU model. The second draws inspiration from PoseFix [23] and introduces synthetic perturbations to mimic common prediction errors, as discussed in [67], where the authors propose a contextual instance decoupling technique to enhance multi-person pose estimation under challenging scenarios. This hybrid sampling strategy not only diversifies the training data but also substantially enhances the model's generalization under real-world uncertainty. Figure 14 illustrates the BUCTD framework for multi-person pose estimation. This design is particularly effective in crowded and complex environments. The core innovation of BUCTD lies in leveraging bottom-up pose predictions as explicit guidance to inform the top-down pose estimation process.