Depth Sensor-Based Facial and Body Animation Control

Reference work entry

Abstract

Depth sensors have become one of the most popular means of generating human facial and posture information in the past decade. By coupling a depth camera and computer vision based recognition algorithms, these sensors can detect human facial and body features in real time. Such a breakthrough has fused many new research directions in animation creation and control, which also has opened up new challenges. In this chapter, we explain how depth sensors obtain human facial and body information. We then discuss on the main challenge on depth sensor-based systems, which is the inaccuracy of the obtained data, and explain how the problem is tackled. Finally, we point out the emerging applications in the field, in which human facial and body feature modeling and understanding is a key research problem.

Keywords

Depth sensors Kinect Facial features Body postures Reconstruction Machine learning Computer animation 

Notes

Acknowledgment

This work is supported by the Engineering and Physical Sciences Research Council (EPSRC) (Ref: EP/M002632/1).

References

  1. Alex Butler D, Izadi S, Hilliges O, Molyneaux D, Hodges S, Kim D (2012) Shake’n’sense: reducing interference for overlapping structured light depth cameras. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI’12. ACM, New York, pp 1933–1936Google Scholar
  2. Bailey SW, Bodenheimer B (2012) A comparison of motion capture data recorded from a vicon system and a Microsoft Kinect sensor. In: Proceedings of the ACM symposium on applied perception, SAP’12. ACM, New York, pp 121–121Google Scholar
  3. Bleiweiss A, Eshar D, Kutliroff G, Lerner A, Oshrat Y, Yanai Y (2010) Enhanced interactive gaming by blending full-body tracking and gesture animation. In: ACM SIGGRAPH ASIA 2010 Sketches. Seoul, South Korea. ACM, p 34Google Scholar
  4. Bronstein AM, Bronstein MM, Kimmel R (2005) Three-dimensional face recognition. Int J Comput Vision 64(1):5–30CrossRefGoogle Scholar
  5. Chai J, Hodgins JK (2005) Performance animation from low-dimensional control signals. In SIGGRAPH’05: ACM SIGGRAPH 2005 Papers. ACM, New York, pp 686–696Google Scholar
  6. Chang KI, Bowyer KW, Flynn PJ (2006) Multiple nose region matching for 3d face recognition under varying facial expression. IEEE Trans Pattern Anal Mach Intell 28(10):1695–700CrossRefGoogle Scholar
  7. Cui Y, Chang W, Nöll T, Stricker D (2013) Kinectavatar: fully automatic body capture using a single Kinect. In: Proceedings of the 11th international conference on computer vision, vol 2, ACCV’12. Springer-Verlag, Berlin/Heidelberg, pp 133–147Google Scholar
  8. Fernández-Baena A, Susí­n A, Lligadas X (2012) Biomechanical validation of upper-body and lower-body joint movements of Kinect motion capture data for rehabilitation treatments. In: Intelligent Networking and Collaborative Systems (INCoS), 2012 4th International Conference on, pp 656–661Google Scholar
  9. Fernandez-Sanchez EJ, Diaz J, Ros E (2013) Background subtraction based on color and depth using active sensors. Sensors 13(7):8895–915CrossRefGoogle Scholar
  10. Girshick R, Shotton J, Kohli P, Criminisi A, Fitzgibbon A (2011) Efficient regression of general-activity human poses from depth images. In: Computer Vision (ICCV), 2011 I.E. international conference on. Barcelona, Spain. pp 415–422Google Scholar
  11. Ho ESL, Chan JCP, Komura T, Leung H (2013) Interactive partner control in close interactions for real-time applications. ACM Trans Multimedia Comput Commun Appl 9(3):21:1–21:19CrossRefGoogle Scholar
  12. Ho ES, Chan JC, Chan DC, Shum HP, Cheung YM, Yuen PC (2016) Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments. Comput Vis Image Underst 148:97–110.  https://doi.org/10.1111/cgf.12735CrossRefGoogle Scholar
  13. Holden D, Saito J, Komura T, Joyce T (2015) Learning motion manifolds with convolutional autoencoders. In ACM SIGGRAPH ASIA 2015 technical briefs. ACM, Kobe, Japan. 2015 SIGGRAPH ASIAGoogle Scholar
  14. Iwamoto N, Shum HPH, Yang L, Morishima S (2015) Multi-layer lattice model for real-time dynamic character animation. Comput Graph Forum 34(7):99–109CrossRefGoogle Scholar
  15. Jiang Y, Saxena A (2013) Hallucinating humans for learning robotic placement of objects. In: Proceedings of the 13th international symposium on experimental robotics. Springer International Publishing, Heidelberg, pp 921–937Google Scholar
  16. Jiang Y, Koppula H, Saxena A (2013) Hallucinated humans as the hidden context for labeling 3d scenes. In: Proceedings of the 2013 I.E. conference on computer vision and pattern recognition, CVPR’13. IEEE Computer Society, Washington, DC, pp 2993–3000Google Scholar
  17. Kakumanu P, Makrogiannis S, Bourbakis N (2007) A survey of skin-color modeling and detection methods. Pattern Recogn 40(3):1106–22CrossRefMATHGoogle Scholar
  18. Kazemi V, Keskin C, Taylor J, Kohli P, Izadi S (2014) Real-time face reconstruction from a single depth image. In: 3D Vision (3DV), 2014 2nd international conference on, vol 1. IEEE, Lyon, France. 2014 3DV. pp 369–376Google Scholar
  19. Kyan M, Sun G, Li H, Zhong L, Muneesawang P, Dong N, Elder B, Guan L (2015) An approach to ballet dance training through ms Kinect and visualization in a cave virtual reality environment. ACM Trans Intell Syst Technol (TIST) 6(2):23Google Scholar
  20. Li H, Yu J, Ye Y, Bregler C (2013) Realtime facial animation with on-the-fly correctives. ACM Trans Graph 32(4):42–1MATHGoogle Scholar
  21. Liang S, Kemelmacher-Shlizerman I, Shapiro LG (2014) 3d face hallucination from a single depth frame. In: 3D Vision (3DV), 2014 2nd international conference on, vol 1. IEEE, Lyon, France. 2014 3DV. pp 31–38Google Scholar
  22. Liu H, Wei X, Chai J, Ha I, Rhee T (2011) Realtime human motion control with a small number of inertial sensors. In: Symposium on interactive 3D graphics and games, I3D’11. ACM, New York, pp 133–140Google Scholar
  23. Liu Z, Huang J, Bu S, Han J, Tang X, Li X (2016a) Template deformation-based 3-d reconstruction of full human body scans from low-cost depth cameras. IEEE Trans Cybern PP(99):1–14Google Scholar
  24. Liu Z, Zhou L, Leung H, Shum HPH (2016b) Kinect posture reconstruction based on a local mixture of gaussian process models. IEEE Trans Vis Comput Graph 14 pp.  https://doi.org/10.1109/TVCG.2015.2510000
  25. Mackay K, Shum HPH, Komura T (2012) Environment capturing with Microsoft Kinect. In: Proceedings of the 2012 international conference on software knowledge information management and applications, SKIMA’12. Chengdu, China. 2012 SKIMAGoogle Scholar
  26. Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohli P, Shotton J, Hodges S, Fitzgibbon A (2011) Kinectfusion: real-time dense surface mapping and tracking. In: Proceedings of the 2011 10th IEEE international symposium on mixed and augmented reality, ISMAR’11. IEEE Computer Society, Washington, DC, pp 127–136Google Scholar
  27. Pachoulakis I, Kapetanakis K (2012) Augmented reality platforms for virtual fitting rooms. Int J Multimedia Appl 4(4):35CrossRefGoogle Scholar
  28. Plantard P, Shum HP, Multon F (2016a) Filtered pose graph for efficient kinect pose reconstruction. Multimed Tools Appl 1–22.  https://doi.org/10.1007/s11042-016-3546-4
  29. Plantard P, Shum HPH, Multon F (2016b) Ergonomics measurements using Kinect with a pose correction framework. In: Proceedings of the 2016 international digital human modeling symposium, DHM ’16, Montreal, 8 pGoogle Scholar
  30. Sandilands P, Choi MG, Komura T (2012) Capturing close interactions with objects using a magnetic motion capture system and a rgbd sensor. In: Proceedings of the 2012 motion in games. Springer, Berlin/Heidelberg, pp 220–231Google Scholar
  31. Sandilands P, Choi MG, Komura T (2013) Interaction capture using magnetic sensors. Comput Anim Virtual Worlds 24(6):527–38CrossRefGoogle Scholar
  32. Segundo MP, Silva L, Bellon ORP, Queirolo CC (2010) Automatic face segmentation and facial landmark detection in range images. Systems Man Cybern Part B Cybern IEEE Trans 40(5):1319–30CrossRefGoogle Scholar
  33. Shotton J, Girshick R, Fitzgibbon A, Sharp T, Cook M, Finocchio M, … Blake A (2013) Efficient human pose estimation from single depth images. IEEE Trans Pattern Anal Machine Intell 35(12):2821–2840CrossRefGoogle Scholar
  34. Shum HPH (2013) Serious games with human-object interactions using rgb-d camera. In: Proceedings of the 6th international conference on motion in games, MIG’13. Springer-Verlag, Berlin/HeidelbergGoogle Scholar
  35. Shum HPH, Ho ESL (2012) Real-time physical modelling of character movements with Microsoft Kinect. In: Proceedings of the 18th ACM symposium on virtual reality software and technology, VRST’12. ACM, New York, pp 17–24Google Scholar
  36. Shum HPH, Ho ESL, Jiang Y, Takagi S (2013) Real-time posture reconstruction for Microsoft Kinect. IEEE Trans Cybern 43(5):1357–69CrossRefGoogle Scholar
  37. Soh J, Choi Y, Park Y, Yang HS (2013) User-friendly 3d object manipulation gesture using Kinect. In: Proceedings of the 12th ACM SIGGRAPH international conference on virtual-reality continuum and its applications in industry, VRCAI’13. ACM, New York, pp 231–234Google Scholar
  38. Sun M, Kohli P, Shotton J (2012) Conditional regression forests for human pose estimation. In: Computer Vision and Pattern Recognition (CVPR), 2012 I.E. conference on. Providence, Rhode Island. pp 3394–3401Google Scholar
  39. Tautges J, Zinke A, Krüger B, Baumann J, Weber A, Helten T, Müller M, Seidel H-P, Eberhardt B (2011) Motion reconstruction using sparse accelerometer data. ACM Trans Graph 30(3):18:1–18:12CrossRefGoogle Scholar
  40. Vera L, Gimeno J, Coma I, Fernández M (2011) Augmented mirror: interactive augmented reality system based on Kinect. In: Human-Computer Interaction–INTERACT 2011. Springer, Lisbon, Portugal. 2011 INTERACT. pp 483–486Google Scholar
  41. Wang L, Villamil R, Samarasekera S, Kumar R (2012) Magic mirror: a virtual handbag shopping system. In: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 I.E. computer society conference on. IEEE, Rhode Island. 2012 CVPR. pp 19–24Google Scholar
  42. Wang K, Wang X, Pan Z, Liu K (2014) A two-stage framework for 3d facereconstruction from rgbd images. Pattern Anal Mach Intell IEEE Trans 36(8):1493–504CrossRefGoogle Scholar
  43. Weise T, Bouaziz S, Li H, Pauly M (2011) Realtime performance-based facial animation. ACM Trans Graph (TOG) 30:77, ACMCrossRefGoogle Scholar
  44. Zhang P, Siu K, Jianjie Z, Liu CK, Chai J (2014a) Leveraging depth cameras and wearable pressure sensors for full-body kinematics and dynamics capture. ACM Trans Graph 33(6):221:1–221:14Google Scholar
  45. Zhang P, Siu K, Jianjie Z, Liu CK, Chai J (2014b) Leveraging depth cameras and wearable pressure sensors for full-body kinematics and dynamics capture. ACM Trans Graph (TOG) 33(6):221Google Scholar
  46. Zhou Z, Shu B, Zhuo S, Deng X, Tan P, Lin S (2012) Image-based clothes animation for virtual fitting. In: SIGGRAPH Asia 2012 technical briefs. ACM, Singapore. 2012 SIGGRAPH ASIA. p 33Google Scholar
  47. Zhou L, Liu Z, Leung H, Shum HPH (2014) Posture reconstruction using Kinect with a probabilistic model. In: Proceedings of the 20th ACM symposium on virtual reality software and technology, VRST’14. ACM, New York, pp 117–125Google Scholar
  48. Zollhöfer M, Nießner M, Izadi S, Rehmann C, Zach C, Fisher M, Wu C, Fitzgibbon A, Loop C, Theobalt C et al (2014) Real-time non-rigid reconstruction using an rgb-d camera. ACM Trans Graph (TOG) 33(4):156CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Northumbria UniversityNewcastle upon TyneUK

Personalised recommendations