Image generation method and device, image generation model training method and device, equipment and medium

Abstract

The embodiment of the invention discloses an image generation method and device, an image generation model training method and device, equipment and a medium, and belongs to the field of image processing. The method comprises the steps: acquiring a first original image, first structure information corresponding to the first original image and second structure information corresponding to a secondoriginal image; inputting the first original image, the first structure information and the second structure information into an encoder to obtain an apparent feature vector and a structure feature vector output by the encoder; and decoding the apparent feature vector and the structural feature vector through a decoder to obtain a target image output by the decoder, the target image including a target object, the target object having apparent features corresponding to the first object, and the target object having structural features corresponding to the second object. The method can be used for amplifying an image set, and is beneficial for increasing the number of images of a sample set or a training set in an image key point prediction task, so as to improve the prediction effect of a key point prediction model.

Description

technical field [0001] The embodiments of the present application relate to the field of image processing, and in particular to a method, device, device, and medium for image generation and image generation model training. Background technique [0002] When the neural network model is used to process images, insufficient sample space or insufficient number of samples will affect the prediction accuracy of the trained neural network model. [0003] In related technologies, in order to increase the number of samples, the image enhancement methods usually used for images generally include: standardization, geometric changes (such as rotation, flipping, translation), random contrast adjustment, random brightness adjustment, etc., which are used to increase the number of samples in the training set. to improve the predictive performance of the model. [0004] Obviously, the methods of increasing the number of samples in related technologies are all physical processing methods, w...

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Problems solved by technology

[0004] Obviously, the methods of increasing the number of samples in related technologies are all physical processing methods, which will not fundamentally change the internal characteristics of the original image, and cannot amplify new images that are different from the images in the original sample set, that is, the image features in the image has not changed, so the improvement effect on the prediction performance of the neural network model is limited

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