Dynamic Garment Simulation based on Hybrid Bounding Volume Hierarchy

Open access


In order to solve the computing speed and efficiency problem of existing dynamic clothing simulation, this paper presents a dynamic garment simulation based on a hybrid bounding volume hierarchy. It firstly uses MCASG graph theory to do the primary segmentation for a given three-dimensional human body model. And then it applies K-means cluster to do the secondary segmentation to collect the human body’s upper arms, lower arms, upper legs, lower legs, trunk, hip and woman’s chest as the elementary units of dynamic clothing simulation. According to different shapes of these elementary units, it chooses the closest and most efficient hybrid bounding box to specify these units, such as cylinder bounding box and elliptic cylinder bounding box. During the process of constructing these bounding boxes, it uses the least squares method and slices of the human body to get the related parameters. This approach makes it possible to use the least amount of bounding boxes to create close collision detection regions for the appearance of the human body. A spring-mass model based on a triangular mesh of the clothing model is finally constructed for dynamic simulation. The simulation result shows the feasibility and superiority of the method described.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] Liu T Bargteil A W O’Brien J F et al. Fast simulation of mass-spring systems[J]. ACM Transactions on Graphics (TOG) 2013 32(6) 214.

  • [2] Wong T H Leach G Zambetta F. Modelling Bending Behaviour in Cloth Simulation Using Hysteresis[C]. Computer Graphics Forum. 2013 32(8) 183-194.

  • [3] Kavan L Gerszewski D Bargteil A W et al. Physics-inspired upsampling for cloth simulation in games[C]. ACM Transactions on Graphics (TOG). ACM 2011 30(4) 93.

  • [4] Provot X. Deformation constraints in a mass-spring model to describe rigid cloth behaviour [C]. Graphics interface. Canadian Information Processing Society 1995 147-147.

  • [5] Jakobsen T. Advanced character physics[C]. Game Developers Conference 2001 383-401.

  • [6] Miguel E Tamstorf R Bradley D et al. Modeling and estimation of internal friction in cloth[J]. ACM Transactions on Graphics (TOG) 2013 32(6) 212.

  • [7] Eberhardt B Weber A Strasser W. A fast flexible particle-system model for cloth draping[J]. Computer Graphics and Applications IEEE 1996 16(5) 52-59.

  • [8] Volino P Magnenat-Thalmann N. Comparing efficiency of integration methods for cloth simulation[C]. Computer graphics international 2001. Proceedings. IEEE 2001 265-272.

  • [9] Volino P Magnenat-Thalmann N. Implicit midpoint integration and adaptive damping for efficient cloth simulation[J]. Computer Animation and Virtual Worlds 2005 16(3-4) 163-175.

  • [10] Baraff D Witkin A. Large steps in cloth simulation[C]. Computer Graphics (SIGGRAPH’ 98) 1998 43-54.

  • [11] Choi K J Ko H S. Stable but responsive cloth[C]. ACM SIGGRAPH 2005 Courses. ACM 2005 1.

  • [12] Eberhardt B Etzmuß O Hauth M. Implicit-explicit schemes for fast animation with particle systems[M]. Springer Vienna 2000.

  • [13] Bridson R Marino S Fedkiw R. Simulation of clothing with folds and wrinkles[C]. Proceedings of ACM SIGGRAPH/Eurographics symposium on Computer animation. Eurographics Association 2003 28-36.

  • [14] Li Z Li L Zou F. 3D foot and shoe matching based on OBB and AABB[J]. International Journal of Clothing Sciences & stechnology 2013 25(5) 389-399.

  • [15] Feng W Yu Y Kim B. A deformation transformer for real-time cloth animation[J]. Acm Transactions on Graphics 2010 29(4) 157-166.

  • [16] Bischoff S Kobbelt L. Ellipsoid decomposition of 3D-models[C]. Proceedings of International Symposium on 3D Data Processing Visualization and Transmission 2002 480-488.

  • [17] Bergen G. Efficient collision detection of complex deformable models using AABB trees[J]. Journal of Graphics Tools 1997 2(4) 1-13.

  • [18] Hutter M Fuhrmann A. Optimized continuous collision detection for deformable triangle meshes[C]. In Proc. WSCG ’07 2007 25-32.

  • [19] Chang J W Wang W Kim M S. Efficient collision detection using a dual OBB-sphere bounding volume hierarchy[J]. Computer-Aided Design 2010 42(1) 50-57.

  • [20] Jagannathan A Miller E L. Three-dimensional surface mesh segmentation using curvedness-based region growing approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007 29(12) 2195-2204.

  • [21] Hartigan J A Wong M A. Algorithm AS 136: A k-means clustering algorithm[J]. Applied Statistics 1979: 100-108.

  • [22] Dyn N Hormann K Kim S J et al. Optimizing 3D triangulations using discrete curvature analysis[J]. Mathematical methods for curves and surfaces 2001 135-146.

  • [23] Alexa M Behr J Cohen-Or D et al. Computing and rendering point set surfaces[J]. IEEE Transactions on Visualization and Computer Graphics 2003 9(1) 3-15.

Journal information
Impact Factor

IMPACT FACTOR 2018: 0.927
5-year IMPACT FACTOR: 1.016

CiteScore 2018: 1.21

SCImago Journal Rank (SJR) 2018: 0.395
Source Normalized Impact per Paper (SNIP) 2018: 1.044

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 215 112 1
PDF Downloads 106 59 0