The field of 3D human pose estimation and neural networks is moving towards more adaptive and expressive models. Researchers are exploring new architectures, such as Kolmogorov-Arnold Networks (KANs) and graph-based models, to improve the accuracy and interpretability of pose estimation. These models are able to capture complex pose variations and long-range dependencies, making them more effective in handling occlusions and depth ambiguities. Additionally, there is a growing interest in using diffusion-based frameworks and hallucinative techniques to improve temporal coherence and resolve ambiguous motions. Notable papers in this area include: Adaptive graph Kolmogorov-Arnold network for 3D human pose estimation, which introduces an adaptive graph KAN framework that extends KANs to graph-based learning for 2D-to-3D pose lifting. DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation, which combines action-aware reasoning with temporal imagination for 3D pose estimation and demonstrates state-of-the-art performance across all metrics.