Video face cartoon animation generation method

Abstract

The present invention discloses a video face cartoon animation generation method belonging to the image processing technology field. The video face cartoon animation generation method of the present invention comprises the following steps of firstly intercepting a frame of positive and neutral expression image from an input video, carrying out the cartoonlization processing on the neutral expression image, recording the eyebrow contour points, the eye contour points, and the maximum height difference h of the upper and lower eyelids when the eyes open; searching a frame similar with the neutral expression image as an initial conversion frame, based on the characteristic points of the last frame, determining the characteristic point processing of the next frame containing the corresponding conversion processing of the eyebrows, the eyes and the mouth, and synthesizing the converted cartoon images, obtaining the next frame image of the image frame corresponding to the synthesized cartoon images, repeating the above steps, and outputting the multiple frames of continuous cartoon images to generate the cartoon animation. The video face cartoon animation generation method of the present invention is used for the generation of the cartoon animation, is faster in generation speed and better in generation effect.

Description

technical field [0001] The invention belongs to image processing technology, and in particular relates to a method for generating cartoon videos based on human face videos. Background technique [0002] At present, the methods for generating video face cartoon animation mainly include key frame interpolation methods, parameter control methods, muscle model methods, physical model methods, and behavior-driven methods. [0003] The main idea of ​​the key frame interpolation method is to use the detection of face head movement or facial expression changes to obtain the most representative key frames from a large number of video image frames, and perform relevant image processing on them, and convert the processed The key frames form a face sample image library, interpolation is performed between the key frames to obtain the processed image at the intermediate moment, and thus a new expression image between the two expressions can be obtained. The applicability of this algorith...

AI Technical Summary

Problems solved by technology

The existing optical flow method does not track feature points with high accuracy, especially when the texture near the feature points is complex, it is easy to drift, which makes the data of facial expression changes wrong, resulting in the change of cartoon faces and real faces. facial expression changes

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