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The paper introduces ProDiff, a trajectory imputation framework that reconstructs missing movement data using only two endpoints, leveraging prototype learning and a denoising diffusion model to enhance accuracy. It outperforms existing methods, achieving significant improvements in imputation precision and demonstrating strong correlation with real trajectories.