Title: UNSUPERVISED CONVOLUTIONAL NEURAL NETWORKS FOR ACCURATE VIDEO FRAME INTERPOLATION WITH INTEGRATION OF MOTION COMPONENTS. Abstract: Optical flow and video frame interpolation are considered as a chicken-egg problem such that one problem affects the other and vice versa. This paper presents a deep neural network that integrates the flow network into the frame interpolation problem, with end-to-end learning. The proposed approach exploits the relationship between the two problems for quality enhancement of interpolation frames. Unlike recent convolutional neural networks, the proposed approach learns motions from natural video frames without graphical ground truth flows for training. This makes the network learn from extensive data and improve the performance. The motion information from the flow network guides interpolator networks to be trained to synthesize the interpolated frame accurately from motion scenarios. In addition, diverse datasets to cover various challenging cases that previous interpolations usually fail in is used for comparison. In all experimental datasets, the proposed network achieves better performance than state-of-art CNN based interpolations. With Middebury benchmark, compared with the top-ranked algorithm, the proposed network reduces an average interpolation error by about 9.3%. The proposed interpolation is ranked the 1st in Standard Deviation (SD) interpolation error, the 2nd in Average Interpolation Error among over 150 algorithms listed in the Middlebury interpolation benchmark.