A 3D Human Body Modeling Method Based on OpenGL and Deep Learning

A deep learning and human body modeling technology, applied in the field of human body 3D modeling, can solve problems such as consuming a lot of manpower, material and financial resources, ordinary users cannot easily obtain 3D models, and restricting 3D human models, so as to improve efficiency and simplify effect of the process

Active Publication Date: 2022-07-29
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

The first method is limited by the high price of 3D laser scanners, and is currently generally used by laboratories or companies
The 3D editing software in the second method is generally oriented to professional 3D model design. Making a 3D model with individual shape features requires professional artists and consumes a lot of manpower, material resources and financial resources.
The shortcomings of the above two typical technologies severely limit the generation of 3D human body models, and ordinary users cannot easily obtain their own 3D models

Method used

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  • A 3D Human Body Modeling Method Based on OpenGL and Deep Learning
  • A 3D Human Body Modeling Method Based on OpenGL and Deep Learning
  • A 3D Human Body Modeling Method Based on OpenGL and Deep Learning

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[0040] Below in conjunction with the accompanying drawings and specific embodiments, the present invention will be further clarified. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. Modifications in the form of valence all fall within the scope defined by the appended claims of the present application.

[0041] A 3D human body modeling method based on OpenGL and deep learning, using the popular deep learning model MaskR-CNN to extract human images in 2D images, and combining OpenGL and standard 3D human body models to reconstruct a personalized 3D human body model Methods. First, the Mask R-CNN deep learning model is used to segment the human body image in the two-dimensional image, and then the main features of the human body contour obtained after the segmentation are extracted. 3D standard human body model, and quickly build a 3D human body model in OpenGL, such as ...

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Abstract

The invention discloses a three-dimensional human body modeling method based on OpenGL and deep learning. The deep learning model Mask R-CNN is used to extract a human body image in a two-dimensional image, and OpenGL and a standard 3D human body model are combined to reconstruct a personalized human body model. Methods for 3D Human Body Models. First, the Mask R‑CNN deep learning model is used to segment the human body image in the two-dimensional image, and then the main features of the human body contour obtained after segmentation are extracted. 3D standard human body model, and quickly build a 3D human body model in OpenGL. The invention not only has fast image processing, but also has high model generation efficiency.

Description

technical field [0001] The invention relates to a three-dimensional human body model established by using a deep learning model and OpenGL, and belongs to the technical field of human three-dimensional modeling. Background technique [0002] The generation of 3D human models refers to the creation of digital geometric models of human objects in virtual scenes. Generating high-fidelity 3D human models with individual shape features is a classic problem that has been studied since the birth of computer graphics, and it is still a hot issue in academia and industry today. There are two reasons for this fact: On the one hand, 3D human models have high complexity. On the other hand, users can easily perceive how distorted the generative model is. [0003] The traditional 3D model generation technology is difficult to provide practical human modeling methods to mass users. Current 3D model generation techniques can be mainly divided into two categories: [0004] (1) Obtain the...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T17/00G06N3/04
CPCG06T17/00G06N3/045
Inventor 贾柯阳高宇周宁宁
Owner NANJING UNIV OF POSTS & TELECOMM
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