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Handbook of Human Motion pp 1–13Cite as

Data-Driven Hand Animation Synthesis

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Abstract

As virtual characters are becoming more and more realistic, the need for recording and synthesizing detailed animations for their hands is increasing. Whether we watch virtual characters in a movie, communicate with an embodied conversational agent in real time, or steer an agent ourselves in a virtual reality application or in a game, detailed hand motions have an impact on how we perceive the character. In this chapter, we give an overview of current methods to record and synthesize the subtleties of hand and finger motions. The approaches we present include marker-based and markerless optical systems, depth sensors, and sensored gloves to capture and record hand motions and data-driven algorithms to synthesize movements when only the body or arm motions are known. We furthermore describe the complex anatomy of the hand and how it is being simplified and give insights on our perception of hand motions to convey why creating realistic hand motions is challenging.

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References

  • Andrews S, Jarvis M, Kry PG (2013) Data-driven fingertip appearance for interactive hand simulation. In: Proceedings of motion on games, MIG ‘13, Dublin, pp 155:177–155:186

    Google Scholar 

  • Argelaguet F, Hoyet L, Trico M, Lecuyer A (2016) The role of interaction in virtual embodiment: effects of the virtual hand representation. In: IEEE virtual reality (VR), Greenville, pp 3–10

    Google Scholar 

  • Aydin Y, Nakajima M (1999) Database guided computer animation of human grasping using forward and inverse kinematics. Comput Graph 23(1):145–154. doi:10.1016/S0097-8493(98)00122-8

    Article  Google Scholar 

  • Braido P, Zhang X (2004) Quantitative analysis of finger motion coordination in hand manipulative and gestic acts. Hum Mov Sci 22(6):661–678. doi:10.1016/j.humov.2003.10.001

    Article  Google Scholar 

  • Chaminade T, Hodgins J, Kawato M (2009) Anthropomorphism influences perception of computer-animated characters’ actions. Soc Cogn Affect Neurosci 2(3):206–216

    Article  Google Scholar 

  • Ciocarlie M, Goldfeder C, Goldfeder C (2007) Dimensionality reduction for hand-independent dexterous robotic grasping. In: IEEE/RSJ international conference on intelligent robots and systems, IROS 2007, San Diego, pp 3270–3275

    Google Scholar 

  • Cutting J, Kozlowski L (1977) Recognizing friends by their walk: gait perception without familiarity cues. Bull Psychon Soc 9(5):353–356

    Article  Google Scholar 

  • de La Gorce M, Paragios N, Fleet DJ (2008) Model-based hand tracking with texture, shading and self-occlusions. In: IEEE conference on computer vision and pattern recognition, Anchorage, pp 1–8

    Google Scholar 

  • Dipietro L, Sabatini A, Dario P (2008) A survey of glove-based systems and their applications. IEEE Trans Syst Man Cybern Part C Appl Rev 38(4):461–482

    Article  Google Scholar 

  • Ebrahimi E, Babu SV, Pagano CC, Jörg S (2016) An empirical evaluation of visuo-haptic feedback on physical reaching behaviors during 3D interaction in real and immersive virtual environments. ACM Trans Appl Percept 13(4):19:1–19:21

    Google Scholar 

  • ElKoura G, Singh K (2003) Handrix: animating the human hand. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation, San Diego, pp 110–119

    Google Scholar 

  • Häger-Ross C, Schieber MH (2000) Quantifying the independence of human finger movements: comparisons of digits, hands, and movement frequencies. J Neurosci 20(22):8542–8550

    Google Scholar 

  • Hoyet L, Ryall K, McDonnell R, O’Sullivan C (2012) Sleight of hand: perception of finger motion from reduced marker sets. In: Proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games, I3D ‘12, Costa Mesa, pp 79–86

    Google Scholar 

  • Huenerfauth M, Lu P (2010) Accurate and accessible motion-capture glove calibration for sign language data collection. ACM Trans Access Comput 3(1):2:1–2:32

    Article  Google Scholar 

  • Jörg S (2011) Perception of body and hand animations for realistic virtual characters. Ph thesis, University of Dublin, Trinity College, Dublin

    Google Scholar 

  • Jörg S, O’Sullivan C (2009) Exploring the dimensionality of finger motion. In: Proceedings of the 9th Eurographics Ireland workshop (EGIE 2009), Dublin, pp 1–11

    Google Scholar 

  • Jörg S, Hodgins J, O’Sullivan C (2010) The perception of finger motions. In: Proceedings of the 7th symposium on applied perception in graphics and visualization (APGV 2010), Los Angeles, pp 129–133

    Google Scholar 

  • Jörg S, Hodgins JK, Safonova A (2012) Data-driven finger motion synthesis for gesturing characters. ACM Trans Graph 31(6):189:1–189:7

    Article  Google Scholar 

  • Kahlesz F, Zachmann G, Klein R (2004) Visual-fidelity dataglove calibration. In: Computer graphics international. IEEE Computer Society, Crete, pp 403–410

    Google Scholar 

  • Kang C, Wheatland N, Neff M, Zordan V (2012) Automatic hand-over animation for free-hand motions from low resolution input. In: Motion in games. Lecture notes in computer science, vol 7660. Springer, Berlin/Heidelberg, pp 244–253

    Chapter  Google Scholar 

  • Kendon A (2004) Gesture – visible action as utterance. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kitagawa M, Windsor B (2008) MoCap for artists: workflow and techniques for motion capture. Focal Press, Amsterdam/Boston

    Google Scholar 

  • Kozlowski LT, Cutting JE (1977) Recognizing the sex of a walker from a dynamic point-light display. Percept Psychophys 21(6):575–580

    Article  Google Scholar 

  • Kry PG, Pai DK (2006) Interaction capture and synthesis. ACM Trans Graph 25(3):872–880

    Article  Google Scholar 

  • Li P, Kry PG (2014) Multi-layer skin simulation with adaptive constraints. In: Proceedings of the 7th international conference on motion in games, MIG ‘14, Playa Vista, pp 171–176

    Google Scholar 

  • Lin L, Jörg S (2016) Need a hand?: how appearance affects the virtual hand illusion. In: Proceedings of the ACM symposium on applied perception, SAP ‘16, Anaheim, pp 69–76

    Google Scholar 

  • Liu CK (2008) Synthesis of interactive hand manipulation. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation, Dublin, pp 163–171

    Google Scholar 

  • Liu CK (2009) Dextrous manipulation from a grasping pose. ACM Trans Graph 28(3):3:1–3:6

    Google Scholar 

  • Lu P, Huenerfauth M (2009) Accessible motion-capture glove calibration protocol for recording sign language data from deaf subjects. In: Proceedings of the 11th international ACM SIGAC-CESS conference on computers and accessibility, pp 83–90

    Google Scholar 

  • Ma K, Hommel B (2015a) Body-ownership for actively operated non-corporeal objects. Conscious Cogn 36:75–86

    Article  Google Scholar 

  • Ma K, Hommel B (2015b) The role of agency for perceived ownership in the virtual hand illusion. Conscious Cogn 36:277–288

    Article  Google Scholar 

  • Majkowska A, Zordan VB, Faloutsos P (2006) Automatic splicing for hand and body animations. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation. Boston, MA, USA, pp 309–316

    Google Scholar 

  • McNeill D (1992) Hand and mind: what gestures reveal about thought. The University of Chicago Press, Chicago

    Google Scholar 

  • Menache A (1999) Understanding motion capture for computer animation and video games. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  • Mousas C, Newbury P, Anagnostopoulos CN (2014) Efficient hand-over motion reconstruction. In: Proceedings of the 22nd international conference in Central Europe on computer graphics, visualization and computer vision, WSCG ‘14. Plzen, Czech Republic, pp 111–120

    Google Scholar 

  • Mousas C, Anagnostopoulos CN, Newbury P (2015) Finger motion estimation and synthesis for gesturing characters. In: Proceedings of the 31st spring conference on computer graphics, SCCG ‘15. Smolenice, Slovakia, pp 97–104

    Google Scholar 

  • Napier J (1980) Hands. Pantheon Books, New York

    Google Scholar 

  • Neff M, Seidel HP (2006) Modeling relaxed hand shape for character animation. In: Articulated Motion and deformable objects. Lecture notes in computer science, vol 4069. Springer, Berlin/Heidelberg, pp 262–270

    Google Scholar 

  • Oshita M, Senju Y (2014) Generating hand motion from body motion using key hand poses. In: Proceedings of the 7th international conference on motion in games, MIG ‘14. Playa Vista, CA, USA, pp 147–151

    Google Scholar 

  • Palastanga N, Soames R (2012) Anatomy and human movement – structure and function, 6th edn. Butterworth Heinemann/Elsevier, Edinburgh/New York

    Google Scholar 

  • Parent R (2012) Computer animation: algorithms and techniques, 3rd edn. Morgan Kaufmann, Burlington

    Google Scholar 

  • Perani D, Fazio F, Borghese NA, Tettamanti M, Ferrari S, Decety J, Gilardi MC (2001) Different brain correlates for watching real and virtual hand actions. Neuroimage 14:749–758

    Article  Google Scholar 

  • Pollard NS, Zordan VB (2005) Physically based grasping control from example. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation. Los Angeles, CA, USA, pp 311–318

    Google Scholar 

  • Prachyabrued M, Borst CW (2014) Visual feedback for virtual grasping. In: IEEE symposium on 3D User Interfaces, 3DUI, 2014. Minneapolis, MN, USA, pp 19–26

    Google Scholar 

  • Samadani AA, DeHart BJ, Robinson K, Kulic D, Kubica E, Gorbet R (2011) A study of human performance in recognizing expressive hand movements. In: IEEE international symposium on robot and human interaction communication. Atlanta, GA, USA

    Google Scholar 

  • Santello M, Flanders M, Soechting JF (1998) Postural hand synergies for tool use. J Neurosci 18(23):10,105–10,115

    Google Scholar 

  • Schröder M, Maycock J, Botsch M (2015) Reduced marker layouts for optical motion capture of hands. In: Proceedings of the 8th ACM SIGGRAPH conference on motion in games, MIG ‘15. Paris, France, pp 7–16

    Google Scholar 

  • Sturman DJ, Zeltzer D (1994) A survey of glove-based input. IEEE Comput Graph Appl 14(1):30–39

    Article  Google Scholar 

  • Wang Y, Neff M (2013) Data-driven glove calibration for hand motion capture. In: Proceedings of the 12th ACM SIGGRAPH/Eurographics symposium on computer animation, SCA ‘13. Anaheim, CA, USA, pp 15–24

    Google Scholar 

  • Wang RY, Popović J (2009) Real-time hand-tracking with a color glove. ACM Trans Graph 28(3):63

    Google Scholar 

  • Wang Y, Tree JEF, Walker M, Neff M (2016) Assessing the impact of hand motion on virtual character personality. ACM Trans Appl Percept 13(2):9:1–9:23

    Article  Google Scholar 

  • Wheatland N, Jörg S, Zordan V (2013): Automatic hand-over animation using principle component analysis. In: Proceedings of motion on games, MIG ‘13. Zürich, Switzerland, pp 175:197–175:202. ACM

    Google Scholar 

  • Wheatland N, Wang Y, Song H, Neff M, Zordan V, Jörg S (2015) State of the art in hand and finger modeling and animation. Comput Graph Forum 34(2):735–760

    Article  Google Scholar 

  • Ye Y, Liu CK (2012) Synthesis of detailed hand manipulations using contact sampling. ACM Trans Graph 31(4):245–254

    Article  Google Scholar 

  • Yuan Y, Steed A (2010) Is the rubber hand illusion induced by immersive virtual reality? Virtual Reality Conference (VR). IEEE Computer Soc. Waltham, MA, USA, pp 95–102

    Google Scholar 

  • Zhang J, Hommel B (2016) Body ownership and response to threat. Psychol Res 80(6):1020–1029

    Google Scholar 

  • Zhu Y, Ramakrishnan AS, Hamann B, Neff M (2013) A system for automatic animation of piano performances. Comput Anim Virtual Worlds 24(5):445–457

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, Clemson University, Clemson, SC, USA

    Sophie Jörg

Authors
  1. Sophie Jörg
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Corresponding author

Correspondence to Sophie Jörg .

Editor information

Editors and Affiliations

  1. Motion & More, Barcelona, Spain

    Bertram Müller

  2. Klinik für Orthopädie und Unfallchirurgi, Universitätsklinikum Heidelberg, Heidelberg, Germany

    Sebastian I. Wolf

  3. Institut for Biomechanics und Orthopedic, German Sport University Cologne, Cologne, Germany

    Gert-Peter Brueggemann

  4. Houston, Texas, USA

    Zhigang Deng

  5. ACRISP, FedUniversity, Cremorne, New South Wales, Australia

    Andrew McIntosh

  6. Wilmington, Delaware, USA

    Freeman Miller

  7. Research, C-Motion, Inc., Germantown, Maryland, USA

    William Scott Selbie

Section Editor information

  1. Department of Computer Science,, University of Houston, Houston, TX, USA

    Zhigang Deng PhD

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© 2016 Springer International Publishing AG

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Jörg, S. (2016). Data-Driven Hand Animation Synthesis. In: Müller, B., et al. Handbook of Human Motion. Springer, Cham. https://doi.org/10.1007/978-3-319-30808-1_13-1

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  • DOI: https://doi.org/10.1007/978-3-319-30808-1_13-1

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  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-30808-1

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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