A new method to evaluate the dynamic air gap thickness and garment sliding of virtual clothes during walking

With the development of e-shopping, there is a significant growth in clothing purchases online. However, the virtual clothing fit evaluation is still under-researched. In the literature, the thickness of the air layer between the human body and clothes is a dominant geometric indicator to evaluate the clothing fit. However, such an approach has only been applied to the stationary positions of the mannequin/human body. Physical indicators such as the pressure/tension of a virtual garment fitted on the virtual body in a continuous motion are also proposed for clothing fit evaluation. Neither geometric nor physical evaluations consider the interaction of the garment with the body, e.g., the sliding of the garment along the human body. In this study, a new framework was proposed to automatically determine the dynamic air gap thickness. First, the dynamic dressed character sequence was simulated in three-dimensional (3D) clothing software via importing the body parameters, cloth parameters, and a walking motion. Second, a cost function was defined to convert the garment in the previous frame to the local coordinate of the next frame. The dynamic air gap thickness between clothes and the human body was determined. Third, a new metric, the 3D garment vector field was proposed to represent the movement flow of the dynamic virtual garment, whose directional changes are calculated by cosine similarity. Experimental results show that our method is more sensitive to the small air gap thickness changes compared with state-of-the-art methods, allowing it to more effectively evaluate clothing fit in a virtual environment.

[1]  Charlie C. L. Wang,et al.  Flexible shape control for automatic resizing of apparel products , 2012, Comput. Aided Des..

[2]  Dong-Eun Kim,et al.  A study of scan garment accuracy and reliability , 2015 .

[3]  Agnes Psikuta,et al.  Validation of a novel 3D scanning method for determination of the air gap in clothing , 2015 .

[4]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[5]  Kristina Ancutiene,et al.  Virtual try-on technologies in the clothing industry. Part 1: investigation of distance ease between body and garment , 2017 .

[6]  Susan P. Ashdown,et al.  Analysis of Cross Sectional Ease Values for Fit Analysis from 3D Body Scan Data Taken in Working Positions , 2011 .

[7]  Yong-Jin Liu,et al.  A survey on CAD methods in 3D garment design , 2010, Comput. Ind..

[8]  Jovan Popovic,et al.  Automatic rigging and animation of 3D characters , 2007, ACM Trans. Graph..

[9]  Yun-Ja Nam,et al.  Automatic body landmark identification for various body figures , 2011 .

[10]  Agnes Psikuta,et al.  A validation methodology and application of 3D garment simulation software to determine the distribution of air layers in garments during walking , 2018 .

[11]  Geoffrey E. Hinton,et al.  Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.

[12]  Ligang Liu,et al.  Scanning 3D Full Human Bodies Using Kinects , 2012, IEEE Transactions on Visualization and Computer Graphics.

[13]  Jun Li,et al.  The effect of air gaps in moist protective clothing on protection from heat and flame , 2013 .

[14]  Pierre Alliez,et al.  Polygon Mesh Processing , 2010 .

[15]  Agnes Psikuta,et al.  Effect of garment properties on air gap thickness and the contact area distribution , 2015 .

[16]  Susan P. Ashdown,et al.  Investigation of the Validity of 3-D Virtual Fitting for Pants , 2015 .

[17]  Yun-Ja Nam,et al.  Comparative analysis of 3D body scan measurements and manual measurements of size Korea adult females , 2010 .

[18]  J. Baerentzen,et al.  Signed distance computation using the angle weighted pseudonormal , 2005, IEEE Transactions on Visualization and Computer Graphics.

[19]  Ken A. Hawick,et al.  3D Vector-Field Data Processing and Visualisation on Graphical Processing Units , 2012 .

[20]  Pascal Bruniaux,et al.  A template of ease allowance for garments based on a 3D reverse methodology , 2013 .

[21]  Taku Komura,et al.  Personalized 3D mannequin reconstruction based on 3D scanning , 2018 .

[22]  Taku Komura,et al.  Scanning and animating characters dressed in multiple-layer garments , 2017, The Visual Computer.

[23]  Ju Fan,et al.  Study on clothing pressure distribution of calf based on finite element method , 2014 .

[24]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[25]  Karen L. LaBat,et al.  An exploratory study of users’ evaluations of the accuracy and fidelity of a three-dimensional garment simulation , 2013 .

[26]  Sunghee Choi,et al.  Automatic pose-independent 3D garment fitting , 2013, Comput. Graph..

[27]  Xianyi Zeng,et al.  Fit evaluation of virtual garment try-on by learning from digital pressure data , 2017, Knowl. Based Syst..

[28]  Yl Kwok,et al.  An investigation on the validity of 3D clothing simulation for garment fit evaluation , 2011 .

[29]  Jun Li,et al.  Correlation between clothing air gap space and fabric mechanical properties , 2013 .

[30]  Jovan Popovic,et al.  Deformation transfer for triangle meshes , 2004, ACM Trans. Graph..

[31]  Agnes Psikuta,et al.  Quantitative evaluation of air gap thickness and contact area between body and garment , 2012 .

[32]  Tim Weyrich,et al.  Post-processing of Scanned 3D Surface Data , 2004, PBG.

[33]  Kaixuan Liu,et al.  Optimization design of cycling clothes’ patterns based on digital clothing pressures , 2016, Fibers and Polymers.

[34]  Alla Sheffer,et al.  Author manuscript, published in "ACM Transactions on Graphics (2012)" DOI: 10.1145/2185520.2185532 Design Preserving Garment Transfer , 2012 .