1. André Gagalowicz Projet MIRAGES INRIA - Rocquencourt - Domaine de Voluceau 78153 Le Chesnay Cedex E-Mail : Andre.Gagalowicz@inria.fr Tél : 01 39 63 54 08 TOWARDS VIRTUAL TRY-ON TECHNOLOGY

2. TABLE OF CONTENTS I. INTRODUCTION II. CONTEXT II.1. Input II.2. Output III. SIMULATION PROCESS III.1. Numerical model for textile material III.2. Scene creation III.3. Evolution of the system over time IV.RESULTS V.CONCLUSION

3. I. INTRODUCTION Aim : Commercial software in order to buy garments through internet Presentation restricted to the case of WOVEN textiles Limitation to a planar surface approach

4. APPLICATION: VIRTUAL TRY-ON (+ VIRTUAL PROTOTYPING) FUNDING: Big Contract from ANR RNTL (french government) for 3 years started in April 2007 Partners: -TEMAT INDUSTRIES (3D scanner SYMCAD) -LA REDOUTE (biggest French garment distributor) -Nadina Corrado (Fashion designer) -ENSITM (French Institute specialist of the mechanics of textile) -INRIA (MIRAGES project; specialist in garment simulation) Target: produce a first prototype

5. Textiles have a NONLINEAR Behaviour HYSTERETIC

6. TENSION F 

7. SHEAR F 

8. BENDING M K

9. II.1 Input II. CONTEXT

10. III.1 Numerical model for textile material a) Classical mass/spring model (finite elements)

11. II.2 Output Evolution of the system over time - 3D data - images II. CONTEXT

12. III.1 Numerical model for textile materials III.2 Creation of the scene III.3 Evolution of the system over time III. SIMULATION PROCESS

13. III.1 Numerical Model for Textile Material (continued) b) Improved mass/spring model Warp/Weft structure is preserved Mixture of bipolar springs (tension and shear) and quadripolar (angular) springs

14. III.1 Numerical model for textile material (continued) c) 2D pattern Meshing Industrial representation of 2D patterns

15. III.2 Creation of the scene III.2.1. Scene description

16. III.2.2. Garment Confection a) 2D patterns positioned AUTOMATICALLY around the numerical mannequin b)Sewing of 2D patterns c)Gravity is added

17. III-2-2 a: Automatic prepositioning of the garment CRUCIAL for the application and VERY DIFFICULT Our solution solves the problem GEOMETRICALLY The 3D garment appears sewn around the body and with a very small amount of spring deformations (.001 mm of average deformation) The simulator is only used for the final tuning (tremendous reduction of the computing time)

18. How is it done ? THE 3D MANNEQUIN Hypothesis : The body is standing The body has his legs and arms put apart symmetrically

19. LABELLING OF THE 2D PATTERN CONTROL POINTS Example of information which must exist on the 2D pattern : In green, sewing lines In red, measurement lines Blue dots : 2D pattern control points

20. MAPPING OF THE 2D PATTERNS CONTROL POINTS ON THE BODY OF THE MANNEQUIN Flat prepositioning of the 2D pattern : 1st step : projection of the 3D points of the body (corresponding to the control points of the 2D patterns) on the YoZ plane of the mannequin 2nd step : mapping of the 2D pattern mesh on the YoZ plane

21. III.2.2. b 2D pattern sewing 2D patterns are sewn along sewing edges Remark : Ambiguïty of the sewing information on the pattern !

22. III.2.3. Blowing of the Garment around the body

23. III.3. Evolution of the system over time III.3.1. Integration of the law of dynamics III.3.2. Control of the nonlinearity, the viscosity model and of the hysteresis III.3.3. Spatial coherence maintenance

24. III.3.1 Integration of the law of dynamics Fondamental law of dynamics  F ext = m. A + c v Implicit integration method (Baraff) viscosity parameters measured from real textile

25. III.3.2 Control of the nonlinearity and of the hysteresis Nonlinear and hysteretical springs control the KES of textile Validation by simulating Kawabata tests

26. RESULTS ON THE CONTROL OF THE KES INSURE THAT OUR MECHANICAL MODEL MIMICS PRECISELY REAL WARP/WEFT TEXTILE DOES NOT CONTROL COMPRESSION

27. TENSION FITTING 0 100 200 300 400 500 600 00.020.040.060.080.10.12 Virtual measure Physic measure F 

28. SHEAR FITTING Real measure Virtual measure F 

29. BENDING FITTING M K

30. EXPERIMENTAL DETERMINATION OF DAMPING PARAMETERS in THE EQUATION OF DYNAMICS: cV AIM: obtain a total phisical control of the equation of dynamics

31. Damping model (Rayleigh) F = (  M +  K) V M : mass matrix K : stiffness matrix  and  have never been computed precisely before.

32. Rayleigh’s damping model applied for fabric model 3 spring types => 3 stiffness matrices K. K = K bnd + K sh + K tns Bending Shearing Tensile Rayleigh's Model => F damp =(  M+  bnd K bnd +  sh K sh +  tns K tns ) V

33. Identification of Rayleigh’s model parameters(1)

34. Identification of Rayleigh’s model parameters(2)

35. Real fall down

36. Global Minimization F error =MA-MG-F springs -F damp Minimizing ||F error || by differentiating Linear system : A (   bnd  sh  tns ) T =b Numerically A is ill-conditioned => the solution is not stable Use of an iterative minimization algorithm

37. RESULT: Comparison between the real and the virtual FREE-FALL in the VISCOUS part of the trajectory

38. III.3.3 Spatial coherence maintenance Detection of collisions Response to collisions (done implicitly by the integration scheme)

39. Detection of Collisions Optimisation through the use of bounding boxes Use of buckets

40. Response to Collisions: collision avoided IMPLICITELY (BARAFF method)

41. Implementation SGI 02 Unix Workstation C++ Tcl scripts for the scene configuration and kinematics

42. IV. RESULTS

43. V. CONCLUSION Numerous soft objects have the same behaviour as textiles Example : Muscular tissues,… Extension to the volumetric case is STRAIGHTFORWARD but requires HEAVY computations actually

44. SOME SIMULATION RESULTS

45. CONTINUED

46. BUCKLING MODELING

47. STUDY OF BUCKLING (REAL)

48. STUDY OF BUCKLING (SIMULATED)