The field of human motion reconstruction and analysis is rapidly advancing, with a focus on developing more accurate and efficient methods for capturing and understanding human movement. Recent developments have seen a shift towards leveraging scene geometry and human-object interactions to improve reconstruction accuracy, as well as the use of adaptive rendering techniques to enhance rendering speed. Additionally, there is a growing interest in developing unified representations of human pose and motion, enabling more effective multi-modal fusion and generation. Noteworthy papers in this area include SHARE, which introduces a technique for accurately grounding human motion reconstruction using scene geometry, and PRGCN, which proposes a novel framework for 3D human pose estimation using a graph memory network. Other notable papers include FootFormer, which achieves state-of-the-art performance in estimating stability-predictive components from visual input, and PPMStereo, which introduces a memory buffer for modeling long-range spatio-temporal consistency in dynamic stereo matching.