Patent ID: 11875601
Assignee: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured for causing a computer to perform operations of:
determining a plurality of second expression images corresponding to a target face image based on a plurality of first expression images contained in a first meme;
generating a second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image;
wherein the determining the plurality of second expression images corresponding to the target face image based on the plurality of first expression images contained in the first meme comprises:
determining an affine transformation parameter between the target face image and an i-th first expression image in the plurality of first expression images according to a corresponding relation between a face key point in the target face image and a face key point in the i-th first expression image, wherein i is a positive integer; and
transforming the target face image based on the affine transformation parameter to obtain an i-th second expression image corresponding to the target face image,
wherein the generating the second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image comprises:
respectively processing the plurality of second expression images by using a preset model, to obtain a plurality of target expression images corresponding to the target face image; and
obtaining the second meme based on the plurality of target expression images corresponding to the target face image,
wherein the computer instructions are further configured for causing the computer to perform an operation of:
performing training by using a plurality of third expression images contained in a third meme to obtain the preset model,
wherein the performing the training by using the plurality of third expression images contained in the third meme to obtain the preset model comprises:
determining an affine transformation parameter between an n-th third expression image in the third meme and an m-th third expression image in the third meme according to a corresponding relation between a face key point in the n-th third expression image and a face key point in the m-th third expression image, wherein both n and m are positive integers;
transforming the n-th third expression image based on the affine transformation parameter to obtain an input sample image, and taking the m-th third expression image as an output sample image; and
performing the training based on the input sample image and the output sample image to obtain the preset model.