Motion in clinical positron emission tomography (PET) examinations degrades image quality and quantification, requiring tailored correction strategies. Recent advancements integrate external devices and/or data-driven motion tracking with image registration and motion modeling, particularly deep learning-based methods, to address complex motion scenarios. The development of total-body PET systems with long axial field-of-view enables advanced motion correction by leveraging extended coverage and continuous acquisition. These innovations enhance the accuracy of motion estimation and correction across various clinical applications, improve quantitative reliability in static and dynamic imaging, and enable more precise assessments in oncology, neurology, and cardiovascular PET studies.