Abstract
Videos captured in the wild often suffer from rain streaks, blur, and noise. In addition, even slight changes in camera pose can amplify cross-frame mismatches and temporal artifacts. Existing methods rely on optical flow or heuristic alignment, which are computationally expensive and less robust. To address these challenges, Lie groups provide a principled way to represent continuous geometric transformations, making them well-suited for enforcing spatial and temporal consistency in video modeling. Building on this insight, we propose DeLiVR, an efficient video deraining method that injects spatiotemporal Lie-group differential biases directly into attention scores of the network. Specifically, the method introduces two complementary components. First, a rotation-bounded Lie relative bias predicts the in-plane angle of each frame using a compact prediction module, where normalized coordinates are rotated and compared with base coordinates to achieve geometry-consistent alignment before feature aggregation. Second, a differential group displacement computes angular differences between adjacent frames to estimate a velocity. This bias computation combines temporal decay and attention masks to focus on inter-frame relationships while precisely matching the direction of rain streaks. Extensive experimental results demonstrate the effectiveness of our method on publicly available benchmarks.
Abstract (translated)
野外拍摄的视频常常会受到雨痕、模糊和噪声的影响。此外，即使是相机姿态的轻微变化也会放大帧间不匹配和时间上的伪影。现有的方法依赖于光学流或启发式对齐方式，这些方法在计算上成本高昂且不够鲁棒。为了解决这些问题，李群（Lie groups）提供了一种原理性的方法来表示连续几何变换，这使得它们非常适合用于确保视频建模中的空间和时间一致性。 基于这一洞察，我们提出了DeLiVR，这是一种高效的去雨迹视频处理方法，它直接在注意力分数中注入了时空李群微分偏差。具体来说，该方法引入了两个互补的组件： 1. **旋转受限李相关偏置**：利用一个紧凑的预测模块预测每个帧的平面内角度，其中归一化坐标被旋转并与基准坐标进行比较，以实现几何一致性对齐前的功能聚合。 2. **微分群位移**：计算相邻帧之间的角差来估计速度。这种偏差计算结合了时间衰减和注意力掩码，专注于帧间关系的同时精确匹配雨迹的方向。 广泛的实验结果证明了我们的方法在公开可用的基准测试中的有效性。
URL
https://arxiv.org/abs/2509.21719
