Advertisement

Virtual 3D Trail Mirror to Project the Image Reality

  • Mattupalli Komal Teja
  • Sajja Karthik
  • Kommu Lavanya Kumari
  • Kothuri Sriraman
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)

Abstract

Magical virtual mirror, is the proposal that is going to present the virtual mirror which is used to display the jewelleries, clothes, footwear’s etc. in the imaginary way means that instead of not wearing physically that in reality. Virtual Mirror uses highly sophisticated 3D image processing techniques to visualize the look of new garments and all other accessories without any need to actually put them on. By using the barcode tag on the clothes or footwear, can easily get the sizes, color, etc. features of that particular item. Previous methods usually involve motion capture, 3D reconstruction; another method based on combining all image-based renderings images of the user and previously recorded garments to save the time processing. Many several existing methods are developed for knowing the outlook of the garment is looked like. No method focusing on exactly fitting of that particular costume or footwear that is actually user interested.

Keywords

Mixed reality Augmented reality Image-based rendering Virtual mirror Intellifit body scanner Wireless barcode scanner Camera 

References

  1. 1.
    Zhang, X., Fan, G.: Dual gait generative models for human motion estimation from a single camera. IEEE Trans. Syst. Man Cybern. B Cybern. 40(4), 1034–1049 (2010)Google Scholar
  2. 2.
    Kanaujia, A., Kittens, N., Ramanathan, N.: Part segmentation of visual hull for 3D human pose estimation. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 542–549 (2013)Google Scholar
  3. 3.
    Liu, Y., Gall, J., Stoll, C., Dai, Q., Seidel, H.-P., Theobalt, C.: Markerless motion capture of multiple characters using multiview image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2720–2735 (2013)Google Scholar
  4. 4.
    Sandhu, R., Dambreville, S., Yezzi, A., Tannenbaum, A.: Non-rigid 2D-3D pose estimation and 2D image segmentation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 786–793 (2009)Google Scholar
  5. 5.
    Hauswiesner, S., Straka, M., Reitmayr, G.: Virtual try-on through image-based rendering. IEEE Trans. Visual. Comput. Graph. 19(9), 1552–1565 (2013)Google Scholar
  6. 6.
    Tanaka, H., Saito, H.: Texture overlay onto flexible object with PCA of silhouettes and k-means method for search into database. In: Proceedings of the IAPR Conference on Machine Vision Applications, Yokohama, Japan (2009)Google Scholar
  7. 7.
    Mansur, A., Makihara, Y., Yagi, Y.: Inverse dynamics for action recognition. IEEE Trans. Cybern. 43(4), 1226–1236 (2013)Google Scholar
  8. 8.
    Elhayek, A., Stoll, C., Hasler, N., Kim, K.I., Seidel, H., Theobalt, C.: Spatio-temporal motion tracking with unsynchronized cameras. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1870–1877Google Scholar
  9. 9.
    Vlasic, D., Baran, I., Matusik W., Popovic, J.: Articulated mesh animation from multi-view silhouettes. ACM Trans. Graph. 27(3), 1–9 (2008)Google Scholar
  10. 10.
    Liu, Y., Stoll, C., Gall, J., Seidel, H.-P., Theobalt, C.: Markerless motion capture of interacting characters using multi-view image segmentation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1249–1256Google Scholar
  11. 11.
    Kanaujia, A., Haering, N., Taylor, G., Bregler, C.: 3D Human pose and shape estimation from multi-view imagery. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 49–56 Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Mattupalli Komal Teja
    • 1
  • Sajja Karthik
    • 1
  • Kommu Lavanya Kumari
    • 1
  • Kothuri Sriraman
    • 1
  1. 1.Department of CSEVignan’s Lara Institute of Technology and ScienceVadlamudi, GunturIndia

Personalised recommendations