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Image generation method based on attention mechanism and flow model

An image generation and attention technology, applied in 2D image generation, image data processing, neural learning methods, etc., can solve problems such as difficulty in taking into account texture information and structural information at the same time, difficulty in retaining clear textures, etc., to improve quality, enrich The effect of detailed information

Pending Publication Date: 2021-11-26
深圳龙岗智能视听研究院
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  • Application Information

AI Technical Summary

Problems solved by technology

This method can usually generate a more accurate structure, but it is difficult to preserve the clear texture of each semantic part of the original image
[0006] All in all, although the current methods can perform better in some aspects, most of them are difficult to take into account both texture information and structural information.

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  • Image generation method based on attention mechanism and flow model
  • Image generation method based on attention mechanism and flow model
  • Image generation method based on attention mechanism and flow model

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Embodiment Construction

[0023] In order to make the purpose, technical method and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific examples. These examples are illustrative only and not limiting of the invention.

[0024] The image generation method based on the attention mechanism and flow model of the present invention uses three subtasks to extract structural information and preserve detail information. The main task uses subtasks.

[0025] The working principle of the method of the present invention is: Subtask 1. Construct a correlation matrix extractor to extract the correlation matrix between the original image and the target posture in order to extract structural information; Subtask 2. Construct a flow field predictor based on the flow model to extract Predict the flow field from the original image to the target pose to preserve the detailed information; and subtask 3. Cons...

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Abstract

The invention discloses an image generation method based on an attention mechanism and a flow model. The method comprises the following steps: S1, preprocessing training data; S2, enabling the preprocessed training data to enter a sub-task I, and extracting a correlation matrix by using an attention mechanism to generate an intermediate result with accurate structure information; S3, enabling the training data to enter a sub-task II, and predicting a flow field by using a flow model to generate an intermediate result with rich detail information; S4, inputting the correlation matrix generated by the sub-task I and the flow field generated by the sub-task II into a sub-task III in a one-to-one correspondence manner under different scales, and generating a weight map by using a content awareness network; and S5, generating results of the subtask 1, the subtask 2 and the subtask 3 and training data enter a main task, a human body image is generated by using an encoder-decoder network, and the image is output. Structural information and detail information can be considered, so that better generation quality is obtained.

Description

technical field [0001] The invention relates to the fields of image generation and generation confrontation network, in particular, an image generation method based on attention mechanism and flow model. Background technique [0002] With the continuous development of deep learning and neural networks, the fields of computer vision and image processing have received extensive attention. Among them, the pose-guided human body image generation task that has emerged in recent years is a challenging and practical topic in the field of computer vision. Its core task is to transform the characters in a given image into the appearance in the target pose through a series of spatial transformations. This task has many difficulties, such as incomplete structural information in the generated image, lack of texture information, and so on. [0003] When the human body image generation task was first proposed, most methods used a simple encoder-decoder structure. However, due to the la...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06N3/02G06N3/08
CPCG06T11/00G06N3/08G06N3/02
Inventor 任俞睿吴玉博龙仕强
Owner 深圳龙岗智能视听研究院
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