The field of 3D human pose estimation is witnessing significant advancements with the integration of innovative techniques such as Transformers, Graph Convolutional Networks (GCNs), and diffusion models. These approaches are being combined in various ways to improve the accuracy and robustness of pose estimation, particularly in complex scenarios and under occlusions. The use of attention mechanisms and uncertainty quantification methods is also becoming increasingly popular, enabling more reliable and efficient pose estimation. Furthermore, the development of lightweight and hybrid architectures is allowing for better performance while reducing computational overhead. Noteworthy papers include:

• A method that exploits the graph modeling capability of GCN to represent each skeleton with multiple graphs of different orders, incorporated with a newly introduced Graph Order Attention module.
• A novel architecture that integrates Transformer, GCN, and diffusion model into a unified framework, achieving state-of-the-art performance on the MPI-INF-3DHP dataset.
• An approach that utilizes Continuous Normalizing Flows to enable dynamic distribution adaptation for uncertainty-aware human pose estimation.