Capsule networks (CapsNets) have recently shown promise to excel in most\ncomputer vision tasks, especially pertaining to scene understanding. In this\npaper, we explore CapsNet's capabilities in optical flow estimation, a task at\nwhich convolutional neural networks (CNNs) have already outperformed other\napproaches. We propose a CapsNet-based architecture, termed FlowCaps, which\nattempts to a) achieve better correspondence matching via finer-grained,\nmotion-specific, and more-interpretable encoding crucial for optical flow\nestimation, b) perform better-generalizable optical flow estimation, c) utilize\nlesser ground truth data, and d) significantly reduce the computational\ncomplexity in achieving good performance, in comparison to its\nCNN-counterparts.\n