The field of trajectory prediction and human mobility is rapidly advancing, with a focus on developing more accurate and efficient models for predicting human behavior in various environments. Researchers are exploring new approaches that incorporate physics-informed constraints, variational mixture models, and cognitive risk integration to improve the accuracy and reliability of trajectory prediction. Additionally, there is a growing interest in using human body pose and social relations to enhance trajectory prediction, as well as integrating high-resolution human mobility data into epidemic modeling. Noteworthy papers in this area include PatchTraj, which proposes a dynamic patch-based trajectory prediction framework, and PhysVarMix, which presents a novel hybrid approach that integrates learning-based with physics-based constraints. Other notable papers include KASportsFormer, which introduces a kinematic anatomy-informed feature representation for 3D human pose estimation, and Social-Pose, which proposes an attention-based pose encoder for predicting human trajectories.