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Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement
by
Petković, Tomislav
, Pribanić, Tomislav
, Bartol, Kristijan
, Bojanić, David
, Peharec, Stanislav
in
Algorithms
/ Analysis
/ Anthropometry
/ body measurement
/ Body measurements
/ Datasets
/ Deep Learning
/ Human Body
/ Humans
/ Linear Models
/ linear regression
/ Measurement
/ Neural Networks, Computer
/ Regression analysis
/ Semantics
/ shape estimation
/ SMPL
/ statistical models
2022
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Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement
by
Petković, Tomislav
, Pribanić, Tomislav
, Bartol, Kristijan
, Bojanić, David
, Peharec, Stanislav
in
Algorithms
/ Analysis
/ Anthropometry
/ body measurement
/ Body measurements
/ Datasets
/ Deep Learning
/ Human Body
/ Humans
/ Linear Models
/ linear regression
/ Measurement
/ Neural Networks, Computer
/ Regression analysis
/ Semantics
/ shape estimation
/ SMPL
/ statistical models
2022
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Do you wish to request the book?
Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement
by
Petković, Tomislav
, Pribanić, Tomislav
, Bartol, Kristijan
, Bojanić, David
, Peharec, Stanislav
in
Algorithms
/ Analysis
/ Anthropometry
/ body measurement
/ Body measurements
/ Datasets
/ Deep Learning
/ Human Body
/ Humans
/ Linear Models
/ linear regression
/ Measurement
/ Neural Networks, Computer
/ Regression analysis
/ Semantics
/ shape estimation
/ SMPL
/ statistical models
2022
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Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement
Journal Article
Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement
2022
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Overview
We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art approaches for body measurement from point clouds and images, demonstrate the comparable performance with the best methods, and even outperform several deep learning models on public datasets. The simplicity of the proposed regression model makes it perfectly suitable as a baseline in addition to the convenience for applications such as the virtual try-on. To improve the repeatability of the results of our baseline and the competing methods, we provide guidelines toward standardized body measurement estimation.
Publisher
MDPI AG,MDPI
Subject
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