LiteFlowNet is a computer vision model that estimates optical flow from image pairs. It employs a pyramidal feature extraction, cascaded flow inference with cost volumes and sub-pixel refinement, feature warping layers, and feature-driven local convolution for flow regularization. The model targets real-time performance with improved accuracy over comparable networks like PWC-Net on benchmarks like KITTI.