# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 1 Introduction
### Research Status  
Early approaches to 2D human pose estimation (2D HPE) originated from graphical model-based methods, including the Pictorial Structures Model [7] and the Deformable Part Model [8]. These methods represent the human body as a structured graph of joints and limbs, performing pose inference by modeling spatial dependencies among joints. Although interpretable, these methods rely heavily on handcrafted features and simple geometric representations, limiting their robustness in complex scenes and varied pose configurations. With the advent of deep learning and data-driven paradigms, 2D HPE research has shifted from feature-engineered models to deep neural network-based approaches trained on large-scale datasets. Contemporary mainstream methods are broadly categorized into two paradigms: top-down and bottom-up approaches. Top-down approaches typically decompose the pose estimation process into two sequential stages. In the first stage, a human detector is used to localize all person instances in the image. In the second stage, keypoints are detected independently within each bounding box. These methods provide high localization accuracy and perform particularly well in scenes with few individuals and minimal occlusion. Early representative methods, such as OpenPose [9] and Mask R-CNN [10], introduced structured outputs and established end-to-end learning pipelines for pose estimation. Subsequently, SimpleBaseline [11] simplified the architecture by employing a residual network for feature extraction and directly regressing keypoint heatmaps, achieving consistent performance across multiple benchmarks. HRNet [12] further advanced the field by maintaining high-resolution feature representations throughout the network, thereby mitigating information loss typical of conventional architectures and enhancing multi-scale pose representations. However, the computational cost of top-down methods scales linearly with the number of detected individuals, which limits their efficiency. Moreover, these methods exhibit reduced robustness in crowded scenes and under severe occlusion, with performance heavily dependent on the accuracy of the human detection stage. Bottom-up approaches detect all keypoints across the entire image in a single forward pass and subsequently group them into individual instances using spatial or semantic cues. By bypassing the person detection stage, these methods provide more predictable computational complexity and are better suited for images containing an unknown number of individuals or severe occlusion. Representative approaches include Part Affinity Fields (PAFs) [9] and Associative Embedding (AE) [13]. PAFs learn directional vector fields to connect specific joints, enabling the model to infer part-to-person associations. In contrast, AE generates embedding vectors for each keypoint and groups them based on feature similarity. These strategies enhance the flexibility and robustness of multi-person pose estimation, particularly in crowded scenes. In recent years, HigherHRNet [14] has advanced bottom-up approaches by incorporating pyramid features while preserving low-level image details, thereby significantly improving the detection of small-scale human instances. However, bottom-up approaches still encounter challenges such as inaccurate keypoint assignments and ambiguous part associations, especially in scenes with similar poses or frequent human interactions. In such scenarios, constructing accurate and consistent pose representations remains challenging. To address the limitations of traditional architectures, researchers have proposed several architectural innovations. One research direction introduces Graph Neural Networks (GNNs) to explicitly model keypoint relationships, thereby enforcing structural consistency constraints [15]. Another line of research, inspired by the success of transformers in natural language processing, replaces traditional convolutional backbones with self-attention mechanisms to capture global dependencies across the image.