Continuous bidirectional optical flow for video frame sequence interpolation

Existing optical flow-based frame interpolation frameworks usually suffer from two problems. First, it is difficult to accurately estimate both large motion and fine motion in the optical flow estimation stage. Second, the hole problem and occlusion problem cannot be efficiently solved in the pixel synthesis step. In this paper, we propose a novel optical flowbased frame interpolation framework, which consists of two submodules: optical flow network and pixel synthesis network. In the optical flow network, we estimate bidirectional optical flow sequences iteratively, which makes full use of the continuity of motion and therefore improves the accuracy of the optical flow estimation. Besides, a novel multi-scale architecture is developed to capture finer motions. In the pixel synthesis network, we fuse the statistical information generated during forward warping to solve the hole problem and the occlusion problem. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.