Abstract
Video inpainting or completion methods are more and more popular in recent years. Most methods focus on handling occluded objects and often assume the backgrounds are still textures. Our work desires to deal with dynamic-texture backgrounds by dynamic texture synthesis method. Most works of dynamic texture synthesis is to extent a short video into an infinite one. The connection of synthesis result is usually ignored, and it will bring some problems. For example, the user removes object, and the object pass through the path which object was appeared. Because the general way is to generate a new video to cover the object, the object will disappear in the synthesis result. In order to enhance the connection of synthesis result, we use manifolds learning to observe the correlation in low dimension space. If the coordinates in the low dimension space can be precise, the recover video can get better. Because the dynamic texture is nonlinear, the system uses nonlinear method to replace the traditional linear method. In addition, we retain a small range which outside the object. And the retain parts is used to predict the missing or the removing parts. Once the trajectory of low dimension space is predicted, the coordinates of trajectory use to recover the dynamic texture.