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Training method and device of image reconstruction network model and electronic equipment

A network model and image reconstruction technology, applied in the field of image processing, can solve problems such as image deviation

Active Publication Date: 2020-06-05
BEIJING BYTEDANCE NETWORK TECH CO LTD
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  • Abstract
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  • Application Information

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Problems solved by technology

Therefore, these methods cannot directly generate 3D human body images, and if the intermediate results are inaccurate, the final generated image will be greatly deviated

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  • Training method and device of image reconstruction network model and electronic equipment
  • Training method and device of image reconstruction network model and electronic equipment
  • Training method and device of image reconstruction network model and electronic equipment

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Embodiment Construction

[0043] Embodiments of the present disclosure are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Apparently, the described embodiments are only some of the embodiments of the present disclosure, not all of them. The present disclosure can also be implemented or applied through different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the case of no conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of...

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Abstract

The embodiment of the invention discloses a training method and device for an image reconstruction network model and electronic equipment. The training method of the image reconstruction network modelcomprises the steps: obtaining initial parameters of a to-be-trained image reconstruction network model; obtaining a sample image pair, wherein the sample image pair comprises a three-dimensional image and a two-dimensional image corresponding to the three-dimensional image, and the three-dimensional image is represented by a first vector; converting the two-dimensional image into a second vectorthrough the image reconstruction network model; calculating an error between the second vector and the first vector; and adjusting parameters of the image reconstruction network model based on the error. According to the method, the two-dimensional image is converted into the vector capable of representing the vertex of the three-dimensional image, and the vector of the vertex of the real three-dimensional image is supervised to train the model capable of reconstructing the three-dimensional image from the single two-dimensional image, so that the technical problem that the reconstruction ofthe three-dimensional object depends on an intermediate result in the prior art is solved.

Description

technical field [0001] The present disclosure relates to the field of image processing, in particular to a training method, device and electronic equipment for an image reconstruction network model. Background technique [0002] At present, with the continuous progress of computer technology and the development of multimedia technology, 3D reconstruction technology has become a research hotspot in the field of graphics in recent years. The indirect method refers to the reconstruction of 3D objects from one or more 2D images, including 3D object reconstruction based on statistical models, 3D object reconstruction based on multi-view geometry, 3D object reconstruction based on illumination stereo, and the rapid development in recent years based on 3D object reconstruction with machine learning. [0003] There are a variety of existing 3D human body reconstruction methods based on the SMPL (A Skinned Multi-Person Linear Model) model, such as SMPLify, which usually rely on inte...

Claims

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Application Information

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IPC IPC(8): G06T17/00G06N20/00
CPCG06T17/00G06N20/00
Inventor 李佩易王长虎
Owner BEIJING BYTEDANCE NETWORK TECH CO LTD