The field of 3D human pose estimation and biomechanical modeling is moving towards more accurate and realistic representations of human motion. Researchers are exploring new methods to address the limitations of current models, such as the use of anatomically accurate skeletons and musculoskeletal models. The integration of data-driven learning and physics-based simulation is also becoming increasingly popular, enabling the estimation of both kinematics and kinetics from monocular video. Furthermore, the development of coarse-to-fine frameworks and the incorporation of camera modeling are improving the accuracy and robustness of 3D human pose estimation. Noteworthy papers in this area include:

• MonoMSK, which introduces a hybrid framework for biomechanically realistic 3D human motion estimation from monocular video.
• SKEL-CF, which proposes a coarse-to-fine framework for SKEL parameter estimation and achieves state-of-the-art results on the MOYO dataset.