Abstract: A method includes receiving an input video stream and providing, to a convolutional neural network (CNN), multiple image frames of the video stream including a target pair of consecutive frames, a frame immediately preceding the target pair, and a frame immediately following the target pair. The method includes generating, by the CNN, multiple interpolated image frames by performing 3D space-time convolution on the multiple image frames and outputting a video stream in which the interpolated image frames are inserted between the frames of the target pair. The convolution may include passing a 3D filter over the multiple image frames in common width and height dimensions, and in a depth dimension representing the number of frames. Generating the interpolated image frames may include generating image data for multiple color channels in respective convolutional layers. The CNN may be trained to predict non-linear movements that occur over multiple image frames.