We present SensorFlow, a novel image and sensor fusion framework for robust, high-quality video stabilization. We start with sensor-based pre-stabilization that smooths out large-scale camera motion. A new angular velocity domain optimization has been introduced to achieve frame rate invariance. We then feed the stabilized optical flows into an occlusion-aware 3D CNN that infers dense warp fields to remove residual translation and jitter. To further avoid distortion, we propose a novel masking scheme to determine the disoccluded and dynamic regions in optical flow and inpaint them with spatially smooth flow vectors. Our method is appealing as it shares both the dense warping field's flexibility to correct complex motions and the robustness of sensor data for arbitrarily challenging scenes. We have validated its effectiveness and demonstrated our solution outperforms state-of-the-art alternatives via extensive ablation studies and quantitative comparisons.