Learning Large Motion Estimation from Intermediate Representations with a High-Resolution Optical Flow Dataset Featuring Long-Range Dynamic Motion

With advancements in sensor and display technologies, high-resolution imagery is becoming increasingly prevalent in diverse applications. As a result, optical flow estimation needs to adapt to larger image resolutions, where even moderate movements lead to substantial pixel displacements, making long-range motion estimation more critical than ever. However, existing datasets primarily focus on short-range flow in low-resolution settings, limiting the generalization of models to high-resolution scenarios with large displacements. Additionally, there is a lack of suitable datasets for evaluating model capacity in long-range motion estimation, further hindering progress in this area. To address this, we introduce RelayFlow-4K, high-resolution 4K optical flow dataset designed to capture diverse motion patterns, including long-range intermediate frame flows. While such datasets provide valuable training resources, long-range estimation remains challenging due to increased matching ambiguity. Simply incorporating these datasets does not inherently improve performance. To this end, we propose a novel training framework that integrates matching cost distillation and incremental time-step learning to refine cost volume estimation and stabilize training. Additionally, we leverage the distance map, which measures the distance from unmatched regions to their nearest matched pixels, improving occlusion handling. Our approach significantly enhances long-range optical flow estimation in high-resolution settings. Our datasets and code will be made publicly available.