Kinect Face

x86

Add the following line in Post-build

xcopy “C:\Program Files (x86)\Microsoft SDKs\Windows\v8.0\ExtensionSDKs\Microsoft.Kinect.Face\2.0\Redist\CommonConfiguration\x64\NuiDatabase” “NuiDatabase” /e /y /i /r

 

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Aforge.net FFMPEG for 64 bit

For using aforge.net Video.FFMPEG module,

(1) copy dll files to the executable folder

(2) set .net version to 4.0

(3) Edit app.config file as follow:

<?xml version=”1.0″ encoding=”utf-8″?>
<configuration>
<startup useLegacyV2RuntimeActivationPolicy=”true”>
<supportedRuntime version=”v4.0″ sku=”.NETFramework,Version=v4.0″/>
</startup>
<runtime>
<NetFx40_LegacySecurityPolicy enabled=”true”/>
</runtime>
</configuration>

[Added May 9, 2017]

https://aforgeffmpeg.codeplex.com/

(1) Use x64 dll (AForge.Video.FFMPEG-2.2.5_x64_DOTNETFX4)

(2) Copy all files in the directory of FFmpeg-git-01fcbdf_x64_shared

Parametric Body Shape Modeling

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Figure. Manikin samples generated using the child statistical body shape model

Statistical body shape models allow for an intuitive generation of a subject-specific body shape with a few given individual’s body characteristics such as stature and body weight. This parametric behavior of the model also allows systematic analyses of effects of each parameter on the body shape.

Two statistical methods are generally used for developing the models: principal component analysis (PCA) and multivariate regression analysis. PCA reduces the high dimensionality of the original datasets so that a body information (3D coordinates of vertices, body landmarks, joint locations, and anthropometric data, etc) can be represented with a few value, a.k.a., “PC scores”. These PC scores, which efficiently accounts for the original data distribution, then are associated meaningful parameters like stature and BMI using the multivariate regression analysis.

Dr. Matt Reed and I have come up with various methods to make the models more realistic and applicable. The featured ideas are as follows:

  • Non-rigid registration of scans while preserving anatomical homologies across the scans using RBF and Implicit-surface fitting methods
  • Bootstrapping standardization process to improve the model quality using a rapid PC-based fitting method
  • Advanced regression analysis to describe the expected nonlinearity in body shape change with different postures

These models will find much more applications including anthropometrics, ergonomics, vehicle design, product development, medical diagnosis, apparel design, and etc.

HumanShape.org

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I have launched HumanShape.org, a web portal for various online body shape models developed by UMTRI. Each model introduced in this site was developed based on hundreds of whole body laser scans, anatomical landmarks, and anthropometric data. As the previous online models, all models provide an intuitive way to generate a body shape, and the predicted 3D geometry and related data can be downloaded into a local repository.

Online Child Body Shape Model

Screen Shot 2016-03-12 at 4.48.36 PM

Childshape.org

My colleague, Dr. Matt Reed and I published a public online body shape modeling site Childshape.org. This is the first online child body shape modeling tool. This site provides an intuitive interface to model a child body shape with a few parameters such as stature, body mass index, and sitting height to standing ratio. Users also can download the modeled body shape to a local repository as an STL file. Also, a set of estimated body landmarks including joint locations can be downloaded as well as the anthropometric dimensions of the selected body shape.

The model was developed using statistical methods based on laser scans of 137 children ages 3 to 11. More information about the technique behind can be found in this paper “Parametric body shape model of standing children aged 3–11 years“.