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. The code is publicly available at https://github.com/Shuning0312/ICLR-DeLiVR.

翻译：在自然场景下拍摄的视频常受雨纹、模糊和噪声的影响。此外，相机姿态的微小变化会加剧帧间失配与时间伪影。现有方法依赖光流或启发式对齐，计算成本高且鲁棒性不足。为解决这些挑战，李群为表示连续几何变换提供了理论框架，使其非常适合在视频建模中强制保持时空一致性。基于此，我们提出DeLiVR——一种高效视频去雨方法，将时空李群差分偏置直接注入网络注意力分数中。具体而言，该方法包含两个互补组件：首先，旋转有界的李相对偏置通过紧凑预测模块估计每帧的平面内旋转角，在特征聚合前将归一化坐标旋转并与基准坐标比较，实现几何一致的对齐；其次，差分群位移通过计算相邻帧间的角度差来估计速度。该偏置计算结合时间衰减与注意力掩码，在聚焦帧间关系的同时精确匹配雨纹方向。大量实验结果表明，该方法在公开基准数据集上具有显著有效性。代码已公开于 https://github.com/Shuning0312/ICLR-DeLiVR。