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Inpher: Inferring Physical Properties of Virtual Objects from Mid-Air Interaction

Published:21 April 2018Publication History

ABSTRACT

We present Inpher, a virtual reality system for setting physical properties of virtual objects using mid-air interaction. Users simply grasp virtual objects and mimic their desired physical movement. The physical properties required to fulfill that movement will then be inferred directly from that motion. We provide a 3D user interface that does not require users to have an abstract model of physical properties. Our approach leverages users' real world experiences with physics. We conducted a bodystorming to investigate users' mental model of physics. Based on our iterative design process, we implemented techniques for inferring mass, bounciness and friction. We conducted a case study with 15 participants with varying levels of physics education. The results indicate that users are capable of demonstrating the required interactions and achieve satisfying results.

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References

  1. 2017. Unity 3D. (July 2017). https://unity3d.com/.Google ScholarGoogle Scholar
  2. R. Abraham and J.E. Marsden. 1978. Foundations of Mechanics. AMS Chelsea Pub./American Mathematical Society. https://books.google.co.uk/books?id=4Y-ownk6ilsCGoogle ScholarGoogle Scholar
  3. Priyanshu Agarwal, Suren Kumar, Jason J Corso, and Venkat Krovi. 2011. Estimating dynamics on-the-fly using monocular video. In Proceedings of 4th Annual Dynamic Systems and Control Conference.Google ScholarGoogle ScholarCross RefCross Ref
  4. Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning. 2009. A survey of robot learning from demonstration. Robotics and autonomous systems 57, 5 (2009), 469--483. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. AxeyWorks. 2017. Low Poly: Free Pack. (May 2017). https://www.assetstore.unity3d.com/en/#!/content/58821.Google ScholarGoogle Scholar
  6. Kiran S Bhat, Steven M Seitz, Jovan Popović, and Pradeep K Khosla. 2002. Computing the physical parameters of rigid-body motion from video. In European Conference on Computer Vision. Springer, 551--565. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Aude G Billard, Sylvain Calinon, and Florent Guenter. 2006. Discriminative and adaptive imitation in uni-manual and bi-manual tasks. Robotics and Autonomous Systems 54, 5 (2006), 370--384.Google ScholarGoogle ScholarCross RefCross Ref
  8. Evren Bozgeyikli, Andrew Raij, Srinivas Katkoori, and Rajiv Dubey. 2016. Point&Teleport Locomotion Technique for Virtual Reality. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '16). ACM, New York, NY, USA, 205--216. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Andreas Fender, Jörg Müller, and David Lindlbauer. 2015. Creature teacher: A performance-based animation system for creating cyclic movements. In Proceedings of the 3rd ACM Symposium on Spatial User Interaction. ACM, 113--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. HTC. 2017. Vive. (July 2017). https://www.vive.com.Google ScholarGoogle Scholar
  11. Auke Jan Ijspeert, Jun Nakanishi, and Stefan Schaal. 2002. Movement imitation with nonlinear dynamical systems in humanoid robots. In Robotics and Automation, 2002. Proceedings. ICRA'02. IEEE International Conference on, Vol. 2. IEEE, 1398--1403.Google ScholarGoogle ScholarCross RefCross Ref
  12. Joseph Laszlo, Michiel van de Panne, and Eugene Fiume. 2000. Interactive control for physically-based animation. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques (SIGGRAPH '00). ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, 201--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. C Karen Liu, Aaron Hertzmann, and Zoran Popović. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Transactions on Graphics (TOG) 24, 3 (2005), 1071--1081. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. NVidia. 2017. PhysX. (July 2017). https://developer.nvidia.com/physx-sdk.Google ScholarGoogle Scholar
  15. Antti Oulasvirta, Esko Kurvinen, and Tomi Kankainen. 2003. Understanding contexts by being there: case studies in bodystorming. Personal and ubiquitous computing 7, 2 (2003), 125--134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Nancy S Pollard and Jessica K Hodgins. 2004. Generalizing demonstrated manipulation tasks. In Algorithmic Foundations of Robotics V. Springer, 523--539.Google ScholarGoogle Scholar
  17. Jovan Popović, Steven M Seitz, and Michael Erdmann. 2003. Motion sketching for control of rigid-body simulations. ACM Transactions on Graphics (TOG) 22, 4 (2003), 1034--1054. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jovan Popović, Steven M Seitz, Michael Erdmann, Zoran Popović, and Andrew Witkin. 2000. Interactive manipulation of rigid body simulations. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., 209--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Jia Sheng, Ravin Balakrishnan, and Karan Singh. 2006. An interface for virtual 3D sculpting via physical proxy.. In GRAPHITE, Vol. 6. 213--220. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jia Wang, Owen Leach, and Robert W Lindeman. 2013. DIY World Builder: an immersive level-editing system. In 3D User Interfaces (3DUI), 2013 IEEE Symposium on. IEEE, 195--196.Google ScholarGoogle Scholar
  21. Jiajun Wu, Joseph J Lim, Hongyi Zhang, Joshua B Tenenbaum, and William T Freeman. 2016. Physics 101: Learning Physical Object Properties from Unlabeled Videos. In BMVC.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
      April 2018
      8489 pages
      ISBN:9781450356206
      DOI:10.1145/3173574

      Copyright © 2018 ACM

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      Publication History

      • Published: 21 April 2018

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      CHI '18 Paper Acceptance Rate666of2,590submissions,26%Overall Acceptance Rate6,199of26,314submissions,24%

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