The field of computer vision is rapidly advancing, with significant developments in 3D human pose estimation and visual SLAM. Researchers are exploring new approaches to improve the accuracy and efficiency of these technologies, including the use of multi-view state space modeling, spatio-temporal transformers, and symmetric two-view association. These innovations have the potential to enhance a wide range of applications, from human-computer interaction to robotics and autonomous systems. Notable papers in this area include MV-SSM, which achieves strong generalization in 3D human pose estimation, and ViSTA-SLAM, which operates without requiring camera intrinsics and achieves superior performance in camera tracking and dense 3D reconstruction. WATCH is also noteworthy, as it addresses the challenges of global human motion reconstruction from in-the-wild monocular videos. Additionally, WinT3R and H2OT demonstrate significant improvements in online reconstruction quality and efficiency. Overall, these advancements are pushing the boundaries of what is possible in computer vision and paving the way for new and exciting applications.