发表机构
Hanyang University（汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PickMoment通过连续时间建模统一去模糊与模糊到视频，利用三种监督训练单一模型，在多个基准上达到最先进性能。
AI 中文摘要
运动模糊源于连续清晰信号在有限曝光窗口内的时间积分，然而现有的基于学习的方法回避了这一物理模型，仅预测清晰信号本身：大多数单图像去模糊方法恢复曝光中心的一帧，而模糊到视频方法预测一组固定帧。我们提出PickMoment，一种连续时间重构方法，通过单一确定性模型直接学习曝光任意子区间上的区间平均模糊。借鉴MeanFlow的平均速度公式，我们使用由模糊积分导出的三种监督来训练模型：来自可用子帧的经验重建损失、强制重叠子区间自一致性的可加性损失，以及锚定在零区间极限的清晰帧损失。一个训练好的模型将单图像去模糊、模糊到视频生成和连续时间选时刻恢复统一为对同一网络的不同查询，无需为每个任务单独训练。我们的PickMoment在GoPro和HIDE上达到了生成式去模糊方法中的最先进性能，在RealBlur上与基于恢复的方法相比具有竞争力，并在GoPro-7模糊到视频中实现了最高的逐帧保真度，所有这些都在单次前向传播中完成，无需迭代采样。
英文摘要
Motion blur arises from the temporal integration of a continuous sharp signal over a finite exposure window, yet existing learning-based methods sidestep this physical model and predict only the sharp signal itself: most single-image deblurring methods recover a single frame at the exposure center, while blur-to-video methods predict a fixed set of frames. We introduce PickMoment, a continuous-time reformulation that directly learns the interval-mean blur over arbitrary sub-intervals of the exposure with a single deterministic model. Drawing an analogy to MeanFlow's average-velocity formulation, we train the model with three supervisions derived from the blur integral: an empirical reconstruction loss from available subframes, an additivity loss that enforces self-consistency across overlapping sub-intervals, and a sharp-frame loss anchored at the zero-interval limit. A single trained model unifies single-image deblurring, blur-to-video generation, and continuous-time pick-a-moment recovery as different queries to the same network, with no separate training for each task. Our PickMoment achieves state-of-the-art performance among generative-based deblurring methods on GoPro and HIDE while competitive against restoration-based methods on RealBlur, and the highest per-frame fidelity on GoPro-7 blur-to-video, all in a single forward pass without iterative sampling.
