O2Flow: Object-aware optical flow estimation

Optical flow estimation is a fundamental task in computer vision that aims to obtain dense pixel motion of adjacent frames. It is widely considered a low-level vision task. Existing methods mainly focus on capturing local or global matching clues for pixel motion modeling, while neglecting object-level information. However, human perception of motion is closely linked to high-level object understanding. To introduce the object awareness into the optical flow estimation pipeline, we propose an Object-aware Optical Flow Estimation framework (O${}^2$Flow) comprising two branches: the Object Awareness (OA) Branch and the Flow Prediction (FP) Branch. Specifically, the FP-branch serves the basic optical flow prediction function, while the OA-branch is designed to capture the object-level information, guided by an auxiliary moving object prediction task. Extensive experimental results demonstrate that our method significantly enhances optical flow estimation performance in these challenging regions. Compared with the other two-view methods, O${}^2$Flow achieves state-of-the-art results on the Sintel and KITTI-2015 benchmarks.