# US 2025/0095112 A1 · Image processing method and apparatus, device, and medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095112 A1 - Document ID: US20250095112A1 - Status: Published application - Kind: A1 - Application number: 18/728,209 - Filed: 2023-01-13 - Published: 2025-03-20 - Earliest claimed: 2022-01-25 - Applicants: Beijing Zitiao Network Technology Co., Ltd. - Inventors: Lang CHEN; Siyu ZHOU - Web page: https://patentgrep.com/patent/US20250095112A1 ## CPC - G06T 5/60: using machine learning, e.g. neural networks - G06T 11/00: Two-dimensional [2D] image generation - G06T 11/10: Texturing; Colouring; Generation of textures or colours (retouching, inpainting or scratch removal G06T5/77) - G06T 2207/20081: Training; Learning - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Embodiments of the present disclosure relate to an image processing method and apparatus, a device, and a medium. The method includes: training to generate a first image generator, and training to generate a second image generator, the second image generator being configured to process an input random feature vector to generate a target object image with a second style; respectively processing an input sample feature vector on the basis of the first image generator and the second image generator, to generate a sample image with a first style and a sample image with the second style as paired sample data; and training, on the basis of the paired sample data, a preset model to generate a target image generator, wherein the target image generator is configured to process an input image with the first style to generate an output image with the second style. ## Claims ### Claim 1 An image processing method, comprising: generating a first image generator by training, and generating a second image generator by training, wherein the first image generator is configured to process an input random feature vector to generate a target object image with a first style, and the second image generator is configured to process the input random feature vector to generate a target object image with a second style; processing an input sample feature vector based on the first image generator and the second image generator to generate a sample image with the first style and a sample image with the second style as paired sample data; and training a preset model based on the paired sample data to generate a target image generator, wherein the target image generator is configured to process an input image with the first style to generate an output image with the second style. ### Claim 2 (depends on 1) The image processing method according to claim 1, wherein the generating of the first image generator by training comprises: randomly collecting first object image data with the first style based on a plurality of first preset indicators; and training a parameter of a generative adversarial network based on the first object image data to obtain the first image generator. ### Claim 3 (depends on 2) The image processing method according to claim 2, wherein the plurality of first preset indicators correspond to a plurality of feature dimensions of a target object. ### Claim 4 (depends on 1) The image processing method according to claim 1, wherein the generating of the second image generator by training comprises: collecting second object image data with the second style based on a plurality of second preset indicators; training a network parameter of the first image generator by using the second object image data to obtain a third image generator; determining a first network corresponding to an image resolution less than or equal to a target image resolution and a second network corresponding to an image resolution greater than the target image resolution in the first image generator during a process of up-sampling input feature information by the first image generator; determining a third network corresponding to an image resolution less than or equal to the target image resolution and a fourth network corresponding to an image resolution greater than the target image resolution in the third image generator during a process of up-sampling the input feature information by the third image generator; and fusing the first network with the third network based on a first fusion parameter preset and fusing the second network with the fourth network based on a second fusion parameter preset to obtain the second image generator. ### Claim 5 (depends on 4) The image processing method according to claim 4, wherein the plurality of second preset indicators correspond to a plurality of different dimensions of the second object image data. ### Claim 6 (depends on 1) The image processing method according to claim 1, wherein the processing of the input sample feature vector based on the first image generator and the second image generator to generate the sample image with the first style and the sample image with the second style as the paired sample data comprises: inputting an object real image with the first style into an image encoder pre-trained to extract a first sample feature vector; inputting the first sample feature vector into the first image generator to generate an object reference image with the first style; inputting the first sample feature vector into the second image generator to generate a first target image with the second style; and generating first-type paired sample data with a first proportion preset by using the object reference image with the first style as the sample image with the first style, and the first target image with the second style as the sample image with the second style. ### Claim 7 (depends on 6) The image processing method according to claim 6, further comprising: generating a second-type paired sample data with a second proportion preset by using the object real image with the first style as the sample image with the first style, and the first target image with the second style as the sample image with the second style. ### Claim 8 (depends on 7) The image processing method according to claim 7, further comprising: inputting a second sample feature vector randomly generated into the first image generator to generate an object random image with the first style; inputting the second sample feature vector into the second image generator to generate a second target image with the second style; and generating a third-type paired sample data with a third proportion preset by using the object random image with the first style as the sample image with the first style, and the second target image with the second style as the sample image with the second style. ### Claim 9 (depends on 8) The image processing method according to claim 8, wherein a sum of the first proportion, the second proportion and the third proportion is 1. ### Claim 10 (depends on 4) The image processing method according to claim 4, further comprising: training a parameter of an image encoder based on object image data with the first style and the first image generator to extract a feature vector corresponding to an input real image from the input real image based on the image encoder trained. ### Claim 11 (depends on 1) The image processing method according to claim 1, further comprising: performing deformation compensation on a difference part of a facial key point between the sample image with the first style and the sample image with the second style in the paired sample data; and/or performing mapping compensation on a difference part of a non-facial key point between the sample image with the first style and the sample image with the second style in the paired sample data. ### Claim 12 (depends on 1) The image processing method according to claim 1, wherein the training of the preset model based on the paired sample data to generate the target image generator comprises: training a parameter of a generative adversarial network based on the paired sample data by supervised learning to generate the target image generator, wherein image textures of the sample image with the first style and the sample image with the second style are weighted and fused according to preset weights during a training process to adjust an image texture of the output image. ### Claim 13 (depends on 1) The image processing method according to claim 1, wherein the input random feature vector comprises at least one of a contour feature or a pixel color feature. ### Claim 14 (canceled) ### Claim 15 An electronic device, comprising: a processor; and a memory configured to store executable instructions for the processor; wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to: generate a first image generator by training, and generate a second image generator by training, wherein the first image generator is configured to process an input random feature vector to generate a target object image with a first style, and the second image generator is configured to process the input random feature vector to generate a target object image with a second style; process an input sample feature vector based on the first image generator and the second image generator to generate a sample image with the first style and a sample image with the second style as paired sample data; and train a preset model based on the paired sample data to generate a target image generator, wherein the target image generator is configured to process an input image with the first style to generate an output image with the second style. ### Claim 16 A non-transitory computer-readable storage medium on which a computer program is stored, wherein the computer program is configured to: generate a first image generator by training, and generate a second image generator by training, wherein the first image generator is configured to process an input random feature vector to generate a target object image with a first style, and the second image generator is configured to process the input random feature vector to generate a target object image with a second style; process an input sample feature vector based on the first image generator and the second image generator to generate a sample image with the first style and a sample image with the second style as paired sample data; and train a preset model based on the paired sample data to generate a target image generator, wherein the target image generator is configured to process an input image with the first style to generate an output image with the second style. ### Claim 17 (canceled) ### Claim 18 (depends on 15) The electronic device according to claim 15, wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to: randomly collect first object image data with the first style based on a plurality of first preset indicators; and train a parameter of a generative adversarial network based on the first object image data to obtain the first image generator. ### Claim 19 (depends on 18) The electronic device according to claim 18, wherein the plurality of first preset indicators correspond to a plurality of feature dimensions of a target object. ### Claim 20 (depends on 15) The electronic device according to claim 15, wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to: collect second object image data with the second style based on a plurality of second preset indicators; train a network parameter of the first image generator by using the second object image data to obtain a third image generator; determine a first network corresponding to an image resolution less than or equal to a target image resolution and a second network corresponding to an image resolution greater than the target image resolution in the first image generator during a process of up-sampling input feature information by the first image generator; determine a third network corresponding to an image resolution less than or equal to the target image resolution and a fourth network corresponding to an image resolution greater than the target image resolution in the third image generator during a process of up-sampling the input feature information by the third image generator; and fuse the first network with the third network based on a first fusion parameter preset and fuse the second network with the fourth network based on a second fusion parameter preset to obtain the second image generator. ### Claim 21 (depends on 16) The non-transitory computer-readable storage medium according to claim 16, wherein the computer program is configured to: randomly collect first object image data with the first style based on a plurality of first preset indicators; and train a parameter of a generative adversarial network based on the first object image data to obtain the first image generator. ### Claim 22 (depends on 21) The non-transitory computer-readable storage medium according to claim 21, wherein the plurality of first preset indicators correspond to a plurality of feature dimensions of a target object.