Abstract
We introduce MIORe and VAR-MIORe, two novel multi-task datasets that address critical limitations in current motion restoration benchmarks. Designed with high-frame-rate (1000 FPS) acquisition and professional-grade optics, our datasets capture a broad spectrum of motion scenarios, which include complex ego-camera movements, dynamic multi-subject interactions, and depth-dependent blur effects. By adaptively averaging frames based on computed optical flow metrics, MIORe generates consistent motion blur, and preserves sharp inputs for video frame interpolation and optical flow estimation. VAR-MIORe further extends by spanning a variable range of motion magnitudes, from minimal to extreme, establishing the first benchmark to offer explicit control over motion amplitude. We provide high-resolution, scalable ground truths that challenge existing algorithms under both controlled and adverse conditions, paving the way for next-generation research of various image and video restoration tasks.
Abstract (translated)
我们介绍了两个新颖的多任务数据集MIORe和VAR-MIORe，这两个数据集旨在解决当前运动恢复基准测试中的关键局限性。我们的数据集采用高帧率（1000 FPS）采集技术和专业级光学设备设计，能够捕捉广泛的运动场景，包括复杂的自拍相机移动、动态的多主体交互以及依赖于深度的模糊效果。 通过根据计算出的光流指标自适应地平均帧，MIORe生成了一致的运动模糊，并保留了清晰的输入数据，以用于视频帧插值和光流估计。VAR-MIORe进一步扩展，涵盖了从最小到极端的不同范围的运动幅度，从而建立了第一个能够提供对运动幅度明确控制的基准。 我们提供了高分辨率、可扩展的真实数据，在受控条件和不利条件下都挑战现有的算法，为各种图像和视频恢复任务的下一代研究铺平了道路。
URL
https://arxiv.org/abs/2509.06803
