# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 5 Conclusions and Future Perspectives
This process encourages the model to learn structural and contextual features. This mechanism naturally facilitates the recovery of occluded keypoints. In contrast, contrastive learning frameworks such as MoCo and DINO enhance robustness to viewpoint changes and articulated pose variations. They achieve this by enforcing feature consistency across diverse augmentations. In addition, multimodal self-supervised methods (e.g., CLIP [75]) and cross-task pretraining frameworks (e.g., DINOv2 [76]) have been incorporated into pose estimation pipelines to produce more generalizable visual backbones. 3. Addressing the computational challenges and deployment constraints of pose estimation methods in real-time environments is essential. Although many methods achieve high accuracy on standard GPUs, real-world applications--such as those on mobile devices, wearables, and embedded platforms--face limitations associated with computational resources, latency sensitivity, and power efficiency. Therefore, models must strike a balance between accuracy and efficiency during deployment. Future research should emphasize low-latency inference, computational efficiency, and model compression. It is recommended that studies consistently report key deployment metrics--such as FLOPs, parameter count, model size, and inference latency--along with testing conditions (e.g., hardware platform and input resolution). These standardized reports enable fair comparisons and support practical deployment. In addition, techniques such as model pruning, quantization, knowledge distillation, and neural architecture search (NAS) can be employed to develop lightweight architectures that are suitable for edge devices. These efforts are critical to bridging the gap between academic performance and real-world system integration. 4. Constructing more diverse and densely annotated datasets is essential to advancing human pose estimation. Existing mainstream datasets offer limited diversity in gender, body shape, clothing, occlusion, and motion patterns. This lack of variation restricts model generalization in complex real-world scenarios. To enhance model adaptability in real-world applications, future datasets should focus on both diversity and annotation granularity. Specifically, datasets should include varied camera viewpoints, cultural contexts, and motion types. They should also improve annotation precision and semantic depth. For example, structured information such as 3D occlusion relationships, skeletal topology constraints, and temporal alignment can offer richer priors and more informative training signals. These enhancements are critical to developing more robust and generalizable pose estimation models. In summary, deep learning-based 2D human pose estimation has developed into a rapidly evolving and interdisciplinary research domain. This review serves as a comprehensive reference for researchers and practitioners. It systematically categorizes existing approaches, compares representative models, and analyzes widely adopted evaluation metrics and benchmark datasets. It helps newcomers gain a quick understanding of the current state of the field. Meanwhile, it provides experienced scholars with structured insights into emerging trends and potential research directions. Conceptualization, Z.Z. and S.-Y.S. ; methodology, Z.Z. and S.-Y.S. ; writing--original draft preparation, Z.Z. and S.-Y.S. ; supervision, S.-Y.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. All datasets referenced in this review are publicly available and have been appropriately cited in the manuscript. No proprietary or newly generated data were used in this study. The authors declare no conflicts of interest.