We introduce MIORe and VAR-MIORe, novel multi-task datasets that address critical limitations in current benchmarks for motion restoration tasks. Our datasets capture a broad spectrum of motion scenarios—including complex ego-camera movements, dynamic multi-subject interactions, and depth-dependent blur effects—using high-frame-rate (1000 FPS) acquisition and professional-grade optics. By averaging variable numbers of frames based on computed optical flow metrics, MIORe generates consistent motion blur while preserving sharp inputs for video frame interpolation and optical flow estimation. VAR-MIORe further extends this framework by spanning a variable range of motion magnitudes, from minimal to extreme, establishing the first benchmark of its kind. Together, these datasets provide high-resolution, scalable ground truth that challenges existing algorithms under both controlled and adverse conditions, paving the way for next-generation research in non-uniform deblurring, video interpolation, and optical flow analysis.