Video frame interpolation is a fundamental technique for generating slow-motion effects and performing video frame rate upconversion. Accurate motion estimation is critical for flow-based interpolation methods. However, existing approaches often yield inaccurate optical flow in complex scenarios involving illumination variations or occlusions. These inaccuracies propagate to the final interpolated frames, introducing artifacts. To address this limitation, we optimize initial optical flow estimations. Specifically, a multi-scale feature extraction module first extracts motion features to estimate preliminary optical flow. Subsequently, an optical flow optimization module encodes the flow, extracts latent spatiotemporal motion features, and integrates them with contextual spatiotemporal information. A dedicated network then generates the refined optical flow, resolving inherent inaccuracies. Experimental results demonstrate that our optimization enhances flow details, producing interpolated frames with sharper object boundaries and superior quality.