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Completion Method of 3D Human Motion Data Based on Sparse Expression

A technology of human motion and sparse expression, applied in the field of data completion, can solve problems such as missing marker points in part of the human body, and achieve the effect of fast calculation, stable algorithm, and fast and accurate completion.

Active Publication Date: 2015-08-26
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, even in the current commercial 3D human body motion capture equipment, due to the self-occlusion of the performer's body limbs, the occlusion of clothing, etc., the phenomenon of missing human body part markers often occurs

Method used

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  • Completion Method of 3D Human Motion Data Based on Sparse Expression
  • Completion Method of 3D Human Motion Data Based on Sparse Expression
  • Completion Method of 3D Human Motion Data Based on Sparse Expression

Examples

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Embodiment 1

[0041] It is necessary to perform data completion on the 3D human body running data with repetitive motion and the complex 3D human motion data without repetitive motion. Such as figure 1 Shown is the sparse expression coefficient of a 3D human pose in an over-complete sample dictionary composed of 800 3D human poses, figure 2 , image 3 and Figure 4 It is the performance comparison of this algorithm under the test of repetitive motion human body data, different size sample dictionaries, different missing frame lengths and different missing markers; Figure 5 , Figure 6 and Figure 7 It is the performance comparison result of this algorithm under the test of complex moving human body data, different size sample dictionaries, different missing frame lengths and different missing markers. Below in conjunction with the concrete technical scheme described above illustrate the steps that this example implements, as follows:

[0042] 1) Use the 3D human motion data provided...

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Abstract

The invention discloses a three-dimensional human motion data completing method based on sparse representation. The three-dimensional human motion data completing method is based on a feature that a three-dimensional human posture reestablishes a representation coefficient in an over-complete sample dictionary, and comprises the following steps of: firstly, establishing the over-complete sample dictionary theta by using collected entire three-dimensional human postures comprising various motion types; secondly, dividing fi into a known part posture fi0 and a missing part posture fim according to a known gauge point set and a missing gauge point set in incomplete three-dimensional human postures, and dividing the theta into theta0 and thetam; thirdly, calculating a sparse representation coefficient xtheta of the known part posture fi0 corresponding to the known gauge point set in a corresponding known part over-complete sample dictionary theta0; and finally, calculating the missing part posture fim according to the xtheta obtained by calculation and the know thetam, so that three-dimensional human motion data can be completed. The three-dimensional human motion data completing method is simple, clear and easy to realize, and can be used for accurately completing the incomplete three-dimensional human motion data.

Description

technical field [0001] The invention relates to data completion and sparse expression, in particular to a three-dimensional human motion data completion method based on sparse expression. Background technique [0002] Three-dimensional human motion data is widely used in film and television entertainment, TV advertisements, computer games and other fields, bringing huge economic benefits. However, even in the current commercial 3D human body motion capture equipment, due to the self-occlusion of the performer's body limbs, the occlusion of clothing, etc., the phenomenon of missing part of the human body markers often occurs. In order to solve this problem, various 3D human motion data completion algorithms have been proposed in recent years, and these methods can be roughly divided into the following categories: [0003] 1) Interpolation method [0004] Use linear or spline interpolation function to interpolate the 3D human motion data curve. The feature of this method is...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/20
Inventor 肖俊冯银付庄越挺
Owner ZHEJIANG UNIV
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