# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 1 Introduction  
### Background  
The continuous advancement of computer vision and deep learning has made machine understanding of human behavior a critical research objective. Human pose estimation (HPE) is a central task in this domain, aiming to identify human joint positions and reconstruct skeletal structures from static images or video sequences. Two-dimensional human pose estimation (2D HPE) focuses on detecting keypoints within the image plane. Although it does not recover three-dimensional information, 2D HPE remains challenging, especially under uncontrolled conditions, such as occlusion, multi-person interactions, and background clutter. Compared to 3D approaches, 2D HPE requires less computational power, is more data-accessible, and is easier to deploy on consumer-grade hardware, making it well-suited for both academic research and practical applications.  
Due to its efficiency and practicality, 2D HPE has been widely integrated into numerous downstream tasks as a foundational component. For instance, in video surveillance, poseestimation enables abnormal behavior detection and crowd dynamics modeling. In sports analytics, it contributes to motion decomposition and technique refinement. In intelligent healthcare, it facilitates posture assessment and rehabilitation monitoring [1]. Moreover, the rising demand for contactless interaction and remote healthcare underscores the need for accurate and real-time pose estimation systems.  
In recent years, deep learning has propelled substantial advances in human pose estimation (HPE) by overcoming the constraints of handcrafted-feature methods. Deep-learning architectures, including convolutional neural networks (CNNs [2]) and graph convolutional networks (GCNs [3]), can extract hierarchical image features. This capability enables accurate and robust joint localization under complex conditions [3; 4]. Consequently, 2D HPE performance in multi-person scenes--characterized by high spatial and semantic ambiguity--has improved markedly.  
Moreover, deep learning approaches have demonstrated strong adaptability in addressing occlusion and crowded scenes--two persistent challenges in human pose estimation. Traditional models often struggle when keypoints are partially occluded or spatially overlapping. In contrast, deep models can leverage contextual cues and employ data augmentation techniques to improve robustness. Recent approaches have utilized generative adversarial networks (GANs [5]) and self-supervised learning strategies to synthesize occlusion-aware training data, thereby enhancing model generalization and stability [6]. These techniques not only improve the accuracy of single-person pose estimation but also advance multi-person pose estimation and interaction understanding.  
Despite substantial progress, challenges remain regarding model interpretability and computational efficiency. In safety-critical domains, such as medical diagnostics and public surveillance, model transparency is indispensable. Consequently, developing trustworthy, lightweight deep learning models that deliver reliable predictions under resource constraints has become a primary research objective. Current efforts concentrate on compact architectures that preserve high accuracy while reducing latency and memory consumption, thereby enabling deployment in diverse real-world scenarios.  
In summary, deep learning has fundamentally transformed 2D human pose estimation by improving its accuracy and robustness and broadening its applicability across diverse domains. As research advances, 2D HPE is poised to play an increasingly pivotal role in bridging human behavior analysis and intelligent visual perception.