Abstract
In this paper, we present an innovative framework for a 3D game character to adopt human action sequence style by learning from videos. The framework is demonstrated for kickboxing, and can be applied to other activities in which individual style includes improvisation of the sequence in which a set of basic actions are performed. A video database of a number of actors performing the basic kickboxing actions is used for feature word vocabulary creation using 3D SIFT descriptors computed for salient points on the silhouette. Next an SVM classifier is trained to recognize actions at frame level. Then an individual actor’s action sequence is gathered automatically from the actor’s kickboxing videos and an HMM structure is trained. The HMM, equipped with the basic repertoire of 3D actions created just once, drives the action level behavior of a 3D game character.
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References
Hogue, A., Gill, S., Jenkin, M.: Automated Avatar Creation for 3D Games. In: Future Play, Toronto, Canada, pp. 174–180 (2007)
Hou, J., Wanga, X., Xua, F., Nguyena, V.D., Wua, L.: Humanoid personalized avatar through multiple natural language processing. World Academy of Science, Engineering and Technology 59, 230–235 (2009)
Sucontphunt, T., Deng, Z., Neumann, U.: Crafting personalized facial avatars using editable portrait & photograph example. In: IEEE Virtual Reality Conference, Lafayette, LA, USA, pp. 259–260 (2009)
Li, Y., Wang, T., Shum, H.: Motion texture- a two level statistical model for character motion synthesis. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, San Antonio, Texas, USA, pp. 465–472 (2002)
Brand, M., Hertzmann, A.: Style machines. In: Proceedings of the 27th annual conference on Computer Graphics and Interactive Techniques, SIGGRAPH, New Orleans, Louisiana, USA, pp. 183–192 (2000)
Hsu, E., Pulli, K., Popović, J.: Style translation for human motion. In: Proceedings of the 32nd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, Los Angeles, USA, pp. 1082–1089 (2005)
Yin, K., Pai, D.: Footsee- an interactive animation system. In: Proceedings of the 2003 ACM, SIGGRAPH/Eurographics Symposium on Computer Animation, San Diego, California, USA, pp. 329–338 (2003)
Chai, J., Hodgins, J.: Performance animation from low-dimensional control signals. ACM Transaction on Graphics (24), 686–696 (2005)
Oshita, M., Yoshiya, T.: Learning motion rules for autonomous characters from control logs using support vector machine. In: International Conference on Computer Animation and Social Agents, Saint-Malo, France (2010) (to appear)
Ofli, F., Erzin, E., Yemez, Y., Tekalp, A., Erdem, C.: Unsupervised dance figure analysis from video for dancing avatar animation. In: IEEE International Conference on Image Processing, San Diego, CA, USA, pp. 1484–1487 (2008)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing (28), 976–990 (2010)
Turaga, P., Chellapa, R., Subramhanian, V., Udrea, O.: Machine recognition of human activities- A Survey. IEEE Transactions on Circuits and Systems for Video Technology (18), 1473–1488 (2008)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic, pp. 59–74 (2004)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal Computer Vision 2(60), 91–110 (2004)
Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: ACM Multimedia, Augsburg, Germany, pp. 357–360 (2007)
Klaser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: British Machine Vision Conference, Leeds, UK, pp. 995–1004 (2008)
Gillies, M.: Learning finite-state machine controllers from motion capture data. IEEE Transactions on Computational Intelligence and AI in Games 1(1), 63–72 (2009)
Kovar, L., Gleicher, M., Pighin, F.: Motion Graphs. ACM Transactions on Graphics 21(3), 473–482 (2002)
Arikan, O., Forsyth, D.: Interactive motion generation from examples. ACM Transaction on Graphics 21(3), 483–490 (2002)
Zordan, V., Majkowska, A., Chiu, B., Fast, M.: Dynamic response for motion capture animation. ACM Transaction on Graphics 24(3), 697–701 (2005)
Niebles, J., Wang, H., Li, F.: Unsupervised learning of human action categories using Spatial-Temporal words. In: British Machine Vision Conference, Edinburgh, UK (2006)
Shapewrap Motion Capture System, http://www.motion-capture-system.com/shapewrap.html (retrieved)
Autodesk 3ds Max, http://usa.autodesk.com/adsk/servlet/pc/index?siteID=123112&id=13567410 (retrieved)
Suryavanshi, B.S., Shiri, N., Mudur, S.P.: An Efficient Technique for Mining Usage Profiles Using Relational Fuzzy Subtractive Clustering. In: IEEE WIRI (Web Information Retrieval and Integration), Washington, DC, pp. 23–29 (2005)
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Chen, X., Mendhurwar, K., Mudur, S., Radhakrishnan, T., Bhattacharya, P. (2010). Learning Human Action Sequence Style from Video for Transfer to 3D Game Characters. In: Boulic, R., Chrysanthou, Y., Komura, T. (eds) Motion in Games. MIG 2010. Lecture Notes in Computer Science, vol 6459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16958-8_39
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DOI: https://doi.org/10.1007/978-3-642-16958-8_39
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