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== DOI ==
 
== DOI ==
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http://dx.doi.org/10.1145/1254960.1254986
 
  
 
== Abstract ==
 
== Abstract ==
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Interactive evolutionary design (IED) has the potential to change development processes in industrial design from generating individual solutions to designing parametric models that are employed to create a vast space of possible solutions. This paper describes the current stage of a continuing interdisciplinary project between computer science and industrial design to make IED accessible for designers. The long term goal is to integrate the software into participatory design research methods, since interactive evolutionary design interfaces can be used without extensive training and skills. Everybody can use it to intuitively identify, communicate and explore appealing solutions within a large solution space. A case study is conducted to illustrate initial experiences by applying interactive evolutionary design software to the design process of a small, electric vehicle. The findings of the case study are immediately applied to further develop the software as a new design tool. The main contribution of this article is an explanation of basic techniques for designers/artists to design and shape a solution space.
+
This paper presents an intuitive method for novice users to interactively design custom populations of stylized,
 +
heterogeneous motion, from one input motion clip, thus allowing the user to amplify an existing database of
 +
motions. We allow the user to set up lattice deformers which are used by a genetic algorithm to manipulate
 +
the animation channels of the input motion and create new motion variations. Our interactive evolutionary
 +
design environment allows the user to traverse the available space of possible motions, presents the user with
 +
populations of motion, and gradually converges to a satisfactory set of solutions. Each generated motion
 +
sequence can undergo a motion filtering process subject to user-specified, high-level metrics to produce a
 +
result crafted to fit the designer’s interest.
  
 
== Extended Abstract ==
 
== Extended Abstract ==
Zeile 13: Zeile 19:
  
 
== Used References ==
 
== Used References ==
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Autodesk Maya software. Available at http://www.autodesk.com/maya
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Amaya, K., Bruderlin, A., and Calvert, T. (1996). Emotion
−
+
from motion. In Graphics Interface ’96, pages 222– 229.  
−
Bentley, P.J. From coffee tables to hospitals: Generic evolutionary design. Evolutionary design by computers, ed. P.J. Bentley (Morgan Kaufmann, San Francisco, 1999), 405--423.
 
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Peter J. Bentley , David W. Corne, Creative evolutionary systems, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001 http://dl.acm.org/citation.cfm?id=510349&CFID=588525319&CFTOKEN=29804931
 
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Bezirtzis, B. G. Generative tools used to search the design solution space: Lessons learned from exploring travelers' experience in airports. MFA Thesis, The Ohio State University, Columbus, OH, 2006.
 
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Matthew Lewis , Keith Ruston, Aesthetic geometry evolution in a generic interactive evolutionary design framework, New Generation Computing, v.23 n.2, p.171-179, January 2005  http://dl.acm.org/citation.cfm?id=1167554&CFID=588525319&CFTOKEN=29804931 http://dx.doi.org/10.1007/BF03037493
 
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Lewis, M. Visual aesthetic evolutionary design links. Available at http://accad.osu.edu/~mlewis/aed.html
 
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Pontecorvo, M.S. Designing the Undesigned: Emergence as a tool for design, in Generative Art-- Proceedings of the 1st International Conference GA'98, (Milan, Italy, 1998), 201--220.
 
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Roesler, A., Feil, M., & Woods, D. D. Design is telling (sharing) stories about the future (MediaPaper CD) Cognitive Systems Engineering Laboratory, Institute for Ergonomics, The Ohio State University, Columbus, OH. Available at http://csel.eng.ohio--state.edu/animock
 
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Rowland, D. & Biocca, F. Evolutionary cooperative design methodology: The genetic sculpture park. Leonardo, vol. 35, (2002), 193--196.
 
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Sanders, E. B.-N. Information, Inspiration and Co--Creation, in Proceedings of the 6th International Conference of the European Academy of Design. (University of Arts, Bremen, Germany, 2005).
 
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Sanders, E. B.-N. From user--centered to participatory design approaches. Design and the social sciences, ed. J. Frascara (Taylor & Francis, New York, 2002).
 
  
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Sanders, E. B.-N. Generative Tools for CoDesigning, in Collaborative design: Proceedings of CoDesigning 2000, ed. S. A. R. Scrivener, L. J. Ball & A. Woodcock, (Springer London, New York, 2000), 542.
+
Bezirtzis, B. G., Lewis, M., and Christeson, C. (2007). In-
−
+
teractive evolution for industrial design. In C&C ’07:
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Karl Sims, Artificial evolution for computer graphics, ACM SIGGRAPH Computer Graphics, v.25 n.4, p.319-328, July 1991  http://dl.acm.org/citation.cfm?id=122752&CFID=588525319&CFTOKEN=29804931 http://doi.acm.org/10.1145/127719.122752
+
Proceedings of the 6th ACM SIGCHI Conference on
 +
Creativity & Cognition, pages 183–192, New York,  
 +
NY, USA. ACM.  
  
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Hideyuki Takagi, Interactive Evolutionary Computation as Humanized Computational Intelligence Technology, Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications, p.1, October 01-03, 2001 http://dl.acm.org/citation.cfm?id=683501&CFID=588525319&CFTOKEN=29804931
+
Chi, D., Costa, M., Zhao, L., and Badler, N. (2000). The
 +
emote model for effort and shape. In SIGGRAPH ’00
 +
Proceedings, pages 173–182, New York, NY, USA.
 +
ACM Press/Addison-Wesley Publishing Co.
 +
 
 +
Gleicher, M. (2001). Comparing constraint-based motion
 +
editing methods. Graphical Models, 63(2):107–134.
 +
 
 +
Ko, H. and Badler, N. I. (1996). Animating human locomo-
 +
tion with inverse dynamics. Computer Graphics and
 +
Applications, IEEE, 16(2):50–59.
 +
 
 +
Kovar, L., Gleicher, M., and Pighin, F. (2002). Motion
 +
graphs. In SIGGRAPH ’02 Proceedings, volume 21,
 +
pages 473–482, New York, NY, USA. ACM Press.
 +
 
 +
Kwon, T., Lee, K. H., Lee, J., and Takahashi, S. (2008).
 +
Group motion editing. In SIGGRAPH ’08 Proceed-
 +
ings, pages 1–8, New York, NY, USA. ACM.
 +
 
 +
Li, T.-Y. and Wang, C.-C. (2007). An evolutionary ap-
 +
proach to crowd simulation. In Autonomous Robots
 +
and Agents, pages 119–126.
 +
 
 +
Lim, I. S. and Thalmann, D. (2000). Tournament selec-
 +
tion for browsing temporal signals. In Symposium on
 +
Applied Computing ’00 Proceedings, pages 570–573,
 +
New York, NY, USA. ACM.
 +
 
 +
Marks, J. et al. (1997). Design galleries: a general ap-
 +
proach to setting parameters for computer graphics
 +
and animation. In SIGGRAPH ’97 Proceedings, pages
 +
389–400, New York, NY, USA. ACM Press/Addison-
 +
Wesley Publishing Co.
 +
 
 +
Massive Software (2009). Massive prime. Accessed online
 +
www.massivesoftware.com/prime/.
 +
 
 +
Mcdonnell, R., Larkin, M., Dobbyn, S., Collins, S., and
 +
O’Sullivan, C. (2008). Clone attack! perception of
 +
crowd variety. In SIGGRAPH ’08 Proceedings, vol-
 +
ume 27, pages 1–8, New York, NY, USA. ACM.
 +
 
 +
M ̈uller, A. (2004). Collision avoiding continuation method
 +
for the inverse kinematics of redundant manipulators.
 +
In Robotics and Automation ’04 Proceedings, vol-
 +
ume 2, pages 1593–1598 Vol.2.
 +
 
 +
Musse, S. R. and Thalmann, D. (2001). Hierarchical model
 +
for real time simulation of virtual human crowds. Vi-
 +
sualization and Computer Graphics, IEEE Transac-
 +
tions, 7(2):152–164.
 +
 
 +
Neff, M. and Fiume, E. (2005). Aer: aesthetic explo-
 +
ration and refinement for expressive character anima-
 +
tion. In SCA ’05: Proceedings of the 2005 ACM SIG-
 +
GRAPH/Eurographics symposium on Computer ani-
 +
mation, pages 161–170, New York, NY, USA. ACM
 +
Press.
 +
 
 +
Sims, K. (1993). Interactive evolution of equations for pro-
 +
cedural models. The Visual Computer, 9(8):466–476.
 +
 
 +
Sung, M. (2007). Continuous motion graph for crowd sim-
 +
ulation. In Technologies for E-Learning and Dig-
 +
ital Entertainment, volume 4469, pages 202–213.
 +
Springer Berlin / Heidelberg.
 +
 
 +
Sung, M., Kovar, L., and Gleicher, M. (2005). Fast and
 +
accurate goal-directed motion synthesis for crowds. In
 +
Symposium on Computer Animation ’05 Proceedings,
 +
pages 291–300, New York, NY, USA. ACM Press.
 +
 
 +
Tak, S., Song, O.-Y., and Ko, H.-S. (2002). Spacetime
 +
sweeping: An interactive dynamic constraints solver.
 +
In Computer Animation ’02 Proceedings, pages 261–
 +
271, Washington, DC, USA. IEEE Computer Society.
 +
 
 +
Treuille, A., Cooper, S., and Popovic, Z. (2006). Continuum
 +
crowds. ACM Transactions on Graphics, 25(3):1160–1168.
 +
 
 +
Ventrella, J. (1995). Disney meets darwin-the evolution of
 +
funny animated figures. Computer Animation, 00.
 +
 
 +
Wang, J., Drucker, S. M., Agrawala, M., and Cohen, M. F.
 +
(2006). The cartoon animation filter. ACM Transac-
 +
tions on Graphics, 25(3):1169–1173.
  
  

Aktuelle Version vom 9. Januar 2015, 23:11 Uhr

Reference

Jonathan Eisenmann, Matthew Lewis, Bryan Cline: Interactive Evolutionary Design of Motion Variants. IJCCI 2009: 127-134.

DOI

Abstract

This paper presents an intuitive method for novice users to interactively design custom populations of stylized, heterogeneous motion, from one input motion clip, thus allowing the user to amplify an existing database of motions. We allow the user to set up lattice deformers which are used by a genetic algorithm to manipulate the animation channels of the input motion and create new motion variations. Our interactive evolutionary design environment allows the user to traverse the available space of possible motions, presents the user with populations of motion, and gradually converges to a satisfactory set of solutions. Each generated motion sequence can undergo a motion filtering process subject to user-specified, high-level metrics to produce a result crafted to fit the designer’s interest.

Extended Abstract

Bibtex

Used References

Amaya, K., Bruderlin, A., and Calvert, T. (1996). Emotion from motion. In Graphics Interface ’96, pages 222– 229.

Bezirtzis, B. G., Lewis, M., and Christeson, C. (2007). In- teractive evolution for industrial design. In C&C ’07: Proceedings of the 6th ACM SIGCHI Conference on Creativity & Cognition, pages 183–192, New York, NY, USA. ACM.

Chi, D., Costa, M., Zhao, L., and Badler, N. (2000). The emote model for effort and shape. In SIGGRAPH ’00 Proceedings, pages 173–182, New York, NY, USA. ACM Press/Addison-Wesley Publishing Co.

Gleicher, M. (2001). Comparing constraint-based motion editing methods. Graphical Models, 63(2):107–134.

Ko, H. and Badler, N. I. (1996). Animating human locomo- tion with inverse dynamics. Computer Graphics and Applications, IEEE, 16(2):50–59.

Kovar, L., Gleicher, M., and Pighin, F. (2002). Motion graphs. In SIGGRAPH ’02 Proceedings, volume 21, pages 473–482, New York, NY, USA. ACM Press.

Kwon, T., Lee, K. H., Lee, J., and Takahashi, S. (2008). Group motion editing. In SIGGRAPH ’08 Proceed- ings, pages 1–8, New York, NY, USA. ACM.

Li, T.-Y. and Wang, C.-C. (2007). An evolutionary ap- proach to crowd simulation. In Autonomous Robots and Agents, pages 119–126.

Lim, I. S. and Thalmann, D. (2000). Tournament selec- tion for browsing temporal signals. In Symposium on Applied Computing ’00 Proceedings, pages 570–573, New York, NY, USA. ACM.

Marks, J. et al. (1997). Design galleries: a general ap- proach to setting parameters for computer graphics and animation. In SIGGRAPH ’97 Proceedings, pages 389–400, New York, NY, USA. ACM Press/Addison- Wesley Publishing Co.

Massive Software (2009). Massive prime. Accessed online www.massivesoftware.com/prime/.

Mcdonnell, R., Larkin, M., Dobbyn, S., Collins, S., and O’Sullivan, C. (2008). Clone attack! perception of crowd variety. In SIGGRAPH ’08 Proceedings, vol- ume 27, pages 1–8, New York, NY, USA. ACM.

M ̈uller, A. (2004). Collision avoiding continuation method for the inverse kinematics of redundant manipulators. In Robotics and Automation ’04 Proceedings, vol- ume 2, pages 1593–1598 Vol.2.

Musse, S. R. and Thalmann, D. (2001). Hierarchical model for real time simulation of virtual human crowds. Vi- sualization and Computer Graphics, IEEE Transac- tions, 7(2):152–164.

Neff, M. and Fiume, E. (2005). Aer: aesthetic explo- ration and refinement for expressive character anima- tion. In SCA ’05: Proceedings of the 2005 ACM SIG- GRAPH/Eurographics symposium on Computer ani- mation, pages 161–170, New York, NY, USA. ACM Press.

Sims, K. (1993). Interactive evolution of equations for pro- cedural models. The Visual Computer, 9(8):466–476.

Sung, M. (2007). Continuous motion graph for crowd sim- ulation. In Technologies for E-Learning and Dig- ital Entertainment, volume 4469, pages 202–213. Springer Berlin / Heidelberg.

Sung, M., Kovar, L., and Gleicher, M. (2005). Fast and accurate goal-directed motion synthesis for crowds. In Symposium on Computer Animation ’05 Proceedings, pages 291–300, New York, NY, USA. ACM Press.

Tak, S., Song, O.-Y., and Ko, H.-S. (2002). Spacetime sweeping: An interactive dynamic constraints solver. In Computer Animation ’02 Proceedings, pages 261– 271, Washington, DC, USA. IEEE Computer Society.

Treuille, A., Cooper, S., and Popovic, Z. (2006). Continuum crowds. ACM Transactions on Graphics, 25(3):1160–1168.

Ventrella, J. (1995). Disney meets darwin-the evolution of funny animated figures. Computer Animation, 00.

Wang, J., Drucker, S. M., Agrawala, M., and Cohen, M. F. (2006). The cartoon animation filter. ACM Transac- tions on Graphics, 25(3):1169–1173.


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