Abstract
Digital heritage applications use virtual characters extensively to populate reconstructions of heritage sites in virtual and augmented reality. Creating these believable characters requires a lot of effort. The characters have to be modelled, textured, rigged and animated. In this chapter, we present a framework that captures a point cloud of a real user using multiple depth cameras and subsequently deforms a template mesh to match the captured geometry. The topology of the template mesh is preserved during the deformation process. We compare the measurements of limb lengths and body part ratios with actual corresponding anthropological measurements from the real user, in order to validate our system. Furthermore, we use a single depth camera to capture the motion of a real performer that we can then use to animate the mesh. This semi-automatic process only requires commodity depth cameras (Microsoft Kinect cameras) and no other specialized hardware. We also present extensions to available open-source animation authoring environment in Blender that allow us to synthesize character animation from prerecorded motion data. We then briefly discuss the challenges involved in enhancing the appearance of the characters by the physically based animation of virtual garments.
References
Ahmed N, De Aguiar E, Theobalt C, Magnor M, Seidel HP (2005) Automatic generation of personalized human avatars from multi-view video. In: Proceedings of the ACM symposium on virtual reality software and technology, pp 257–260
Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J (2005) Scape: shape completion and animation of people. ACM Trans Graph 24(3):408–416
Blender (2016) http://www.blender.org
Chang W, Zwicker M (2011) Global registration of dynamic range scans for articulated model reconstruction. ACM Trans Graph 30(3), 26:1–26:15
Cui Y, Chang W, Nll T, Stricker D (2012) Kinectavatar: Fully automatic body capture using a single kinect. In: ACCV workshop on color depth fusion in computer vision
De Aguiar E, Stoll C, Theobalt C, Ahmed N, Seidel, HP, Thrun S (2008) Performance capture from sparse multi-view video. In: ACM Transactions on Graphics, vol 27, p 98
Gall J, Stoll C, De Aguiar E, Theobalt C, Rosenhahn B, Seidel HP (2009) Motion capture using joint skeleton tracking and surface estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp 1746–1753
Gokani M, Chaudhuri P (2011) Motion graphs in blender. In: Proceedings of the 10th annual blender conference
Gokani M, Chaudhuri P (2012) Path synthesis for motion graphs in blender. In: Proceedings of the 11th annual blender conference
Higgett N, Saucedo GM, Baines E, Tatham E (2012) Virtual characters and mobile apps for digital building heritage. http://digitalbuildingheritage.our.dmu.ac.uk/files/2012/06/4-Nick-HiggettVirtual-Characters-and-Mobile-Apps.pdf
Kinect M (2016) https://developer.microsoft.com/en-us/windows/kinect
Kovar L, Gleicher M, Pighin F (2002) Motion graphs. ACM Trans Graph 21(3):473–482
MakeHuman (2016) http://www.makehuman.org/
Mashalkar J, Bagwe N, Chaudhuri P (2013) Personalized animatable avatars from depth data. In: Proceedings of the 5th Joint Virtual Reality Conference, JVRC ’13, pp 25–32
Muralikrishnan S, Chaudhuri P (2016) Sketch-based simulated draping for Indian garments. In: Proceedings of the tenth Indian conference on computer vision, graphics and image processing, ICVGIP ’16, pp 92:1–92:6
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, pp 127–136
Papagiannakis G, Elissavet G, Trahanias P, Tsioumas M (2014) A Geometric algebra animation method for mobile augmented reality simulations in digital heritage sites Springer International Publishing pp 258–267
RealSense I (2016) http://www.intel.in/content/www/in/en/architecture-and-technology/realsense-overview.html
Sengupta S, Chaudhuri P (2013) Virtual garment simulation. In: Fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), pp 1–4
Tong J, Zhou J, Liu L, Pan Z, Yan H (2012) Scanning 3d full human bodies using kinects. IEEE Trans Visual Comput Graphi 18(4)
Weiss A, Hirshberg D, Black MJ (2011) Home 3d body scans from noisy image and range data. In: Proceedings of the 2011 international conference on computer vision, ICCV ’11, pp 1951–1958
Acknowledgements
We would like to thank the MakeHuman [13] project for the human template models and the Blender Foundation for the open-source Blender [3] 3D content creation software. This research was supported by the Immersive Digital Heritage project (NRDMS/11/1586/2009) under the Digital Hampi initiative of the Department of Science and Technology, Government of India.
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Mashalkar, J., Chaudhuri, P. (2017). Creating Personalized Avatars. In: Mallik, A., Chaudhury, S., Chandru, V., Srinivasan, S. (eds) Digital Hampi: Preserving Indian Cultural Heritage. Springer, Singapore. https://doi.org/10.1007/978-981-10-5738-0_17
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