# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 5 Conclusions and Future Perspectives  
This review provides a systematic summary of recent advances in deep learning-based 2D human pose estimation (2D HPE). It covers fundamental methodologies, representative models, evaluation metrics, and widely adopted benchmark datasets. We categorize and analyze approaches for both single-person and multi-person pose estimation. Special attention is given to the evolution of methodologies--from early handcrafted feature-based techniques to deep neural network architectures, and more recently, to attention-based mechanisms and transformer frameworks. Heatmap-based methods remain the dominant paradigm because of their high spatial localization accuracy. Meanwhile, regression-based and hybrid approaches are continuously evolving, especially in terms of lightweight design and end-to-end training efficiency. In multi-person scenarios, top-down and bottom-up paradigms each offer distinct advantages. Top-down methods perform well in relatively clean scenes. In contrast, bottom-up approaches offer greater scalability and robustness in crowded or occluded environments. Hybrid strategies that integrate both paradigms--such as BUCTD [65] and PoseFix [23]--have demonstrated promising potential. These approaches aim to balance accuracy and computational efficiency in complex scenarios. Furthermore, the emergence of lightweight architectures and transformer-based models has created new opportunities. These advances support real-time applications and enable efficient deployment on edge devices. Despite substantial progress in 2D human pose estimation, several critical challenges persist. First, existing models exhibit limited robustness in crowded or heavily occluded scenes. This limitation hinders accurate keypoint detection and association across multiple individuals. Second, most methods heavily rely on curated and standardized datasets. As a result, their ability to generalize to complex, unconstrained real-world environments is limited. Third, although transformer-based architectures have achieved impressive performance, their high computational cost remains a barrier to real-time inference and deployment on edge devices. Lastly, the lack of large-scale, high-quality annotated pose datasets continues to be a major bottleneck to further advancement in this field. To overcome these challenges, future research may focus on several promising directions. 1. To enhance overall efficiency and inter-module synergy, future research should prioritize the development of end-to-end frameworks that integrate object detection, keypoint estimation, and post-processing. Most existing methods rely on multi-stage pipelines, which despite their modularity, are susceptible to error accumulation and exhibit low inference efficiency. A unified architecture that jointly optimizes detection and pose estimation could streamline the training process and improve system consistency. Transformer-based architectures, due to their strong capacity to model global dependencies, have demonstrated promising performance in pose estimation, as evidenced by TokenPose [16] and ViTPose [35]. Building on this foundation, future work may explore query-based architectures inspired by DETR [70], which employ learnable queries to directly predict human instances along with their keypoints. Furthermore, integrating structural priors--such as skeletal graphs or symmetry constraints--may enhance the anatomical plausibility and robustness of predicted poses. 2. Incorporating unsupervised and self-supervised pretraining strategies can reduce dependence on large-scale annotated datasets. These approaches also improve the robustness of keypoint estimation under occlusion. In recent years, masked image modeling techniques (e.g., MAE [71], SimMIM [72]) and contrastive learning methods (e.g., MoCo [73], DINO [74]) have been widely adopted for visual representation learning. Masked image modeling (MIM) approaches, such as MAE and SimMIM, reconstruct masked regions in the input image.