Respiratory motion artifacts severely compromise the diagnostic utility of Cone-Beam CT (CBCT). Motion-compensated (MoCo) reconstruction faces a critical trade-off: fast deformation vector field (DVF) estimation from prior scans can be inaccurate against anatomical changes, while accurate iterative methods are too slow for clinical workflows. We propose a learning-based MoCo framework to resolve this dilemma. It utilizes a deep neural network, trained patient-specifically on a prior 4D-CT, to rapidly and directly infer DVFs from 2D projections. This non-iterative approach leverages a learned motion model to ensure both efficiency and robustness against inter-fractional changes. These DVFs then guide a reconstruction pipeline that corrects motion on a per-projection basis by warping each back-projection. Experiments on phantom and clinical data demonstrate that this approach effectively corrects motion artifacts, yields superior reconstruction quality. Our framework yields high-quality static 3D images and enables full 4D-CBCT synthesis, paving the way for advanced adaptive radiotherapy workflows.