“Capture of hair geometry from multiple images” by Paris, Briceño and Sillion

  • ©Sylvain Paris, Hector M. Briceño, and François X. Sillion




    Capture of hair geometry from multiple images



    Hair is a major feature of digital characters. Unfortunately, it has a complex geometry which challenges standard modeling tools. Some dedicated techniques exist, but creating a realistic hairstyle still takes hours. Complementary to user-driven methods, we here propose an image-based approach to capture the geometry of hair.The novelty of this work is that we draw information from the scattering properties of the hair that are normally considered a hindrance. To do so, we analyze image sequences from a fixed camera with a moving light source. We first introduce a novel method to compute the image orientation of the hairs from their anisotropic behavior. This method is proven to subsume and extend existing work while improving accuracy. This image orientation is then raised into a 3D orientation by analyzing the light reflected by the hair fibers. This part relies on minimal assumptions that have been proven correct in previous work.Finally, we show how to use several such image sequences to reconstruct the complete hair geometry of a real person. Results are shown to illustrate the fidelity of the captured geometry to the original hair. This technique paves the way for a new approach to digital hair generation.


    1. ANJYO, K., USAMI, Y., AND KURIHARA, T. 1992. A simple method for extracting the natural beauty of hair. In Proc. of SIGGRAPH, ACM. Google ScholarDigital Library
    2. BAKER, S., AND NAYAR, S. 1999. Global measures of coherence for edge detector evaluation. In Conference on Computer Vision and Pattern Recognition, vol. 2.Google ScholarCross Ref
    3. BERTAILS, F., KIM, T.-Y., CANI, M.-P., AND NEUMANN, U. 2003. Adaptive wisp tree. In Proc. of Symposium on Computer Animation.Google Scholar
    4. BROOKS, M. J., AND HORN, B. K. P. 1989. Shape and source from shading. In Shape from Shading, B. K. P. Horn and M. J. Brooks, Eds. MIT Press. Google ScholarDigital Library
    5. CANNY, J. 1983. Finding Edges and Lines in Images. Master’s thesis, MIT.Google Scholar
    6. DALDEGAN, A., MAGNENAT-THALMANN, N., KURIHARA, T., AND THALMANN, D. 1993. An integrated system for modeling, animating and rendering hair. Computer Graphics Forum 12, 3, 211–221.Google ScholarCross Ref
    7. DERICHE, R. 1987. Using Canny’s criteria to derive a recursively implemented optimal edge detector. The International Journal of Computer Vision 1 (May).Google Scholar
    8. DONOHO, D., AND HUO, X. 2000. Beamlet pyramids. In Proc. of SPIE conference.Google Scholar
    9. DURAND, F., AND DORSEY, J. 2002. Fast bilateral filtering for the display of high-dynamic-range images. In Proc. of SIGGRAPH, ACM. Google ScholarDigital Library
    10. ELAD, M. 2002. On the bilateral filter and ways to improve it. IEEE Trans. on Image Processing 11, 10 (October), 1141–1151. Google ScholarDigital Library
    11. FEICHTINGER, H. G., AND STROHMER, T., Eds. 2003. Advances in Gabor Analysis. Birkhauser. Google ScholarDigital Library
    12. FELSBERG, M., AND SOMMER, G. 2000. A new extension of linear signal processing for estimating local properties and detecting features. In Proc. of DAGM Symposium Mustererkennung. Google ScholarDigital Library
    13. FORBES, L. A., AND DRAPER, B. A. 2000. Inconsistencies in edge detector evaluation. In Conference on Computer Vision and Pattern Recognition.Google ScholarCross Ref
    14. FREEMAN, W. T., AND ADELSON, E. H. 1991. The design and use of steerable filters. IEEE Trans. Pattern Analysis and Machine Intelligence 13, 9, 891–906. Google ScholarDigital Library
    15. GRABLI, S., SILLION, F., MARSCHNER, S. R., AND LENGYEL, J. E. 2002. Image-based hair capture by inverse lighting. In Proc. Graphics Interface, 51–58.Google Scholar
    16. GRANLUND, G. H., AND KNUTSSON, H. 1995. Signal Processing for Computer Vision. Kluwer Academic Publishers. Google ScholarDigital Library
    17. HADAP, S., AND MAGNENAT-THALMANN, N. 2000. Interactive hair styler based on fluid flow. In Proc. of Workshop on Computer Animation and Simulation, Eurographics.Google ScholarCross Ref
    18. HERTZMANN, A., AND SEITZ, S. 2003. Shape and materials by example: A photometric stereo approach. In Proc. of Conference on Computer Vision and Pattern Recognition, 576–584. Google ScholarDigital Library
    19. ISHIKAWA, H. 2000. Global Optimization Using Embedded Graphs. PhD thesis, New York University. Google ScholarDigital Library
    20. KAJIYA, J. T., AND KAY, T. L. 1989. Rendering fur with three dimensional textures. In Proc. of SIGGRAPH, ACM. Google ScholarDigital Library
    21. KIM, T.-Y., AND NEUMANN, U. 2001. Opacity shadow maps. In Proc. of Rendering Techniques conf., Springer, 177–182. Google ScholarDigital Library
    22. KIM, T.-Y., AND NEUMANN, U. 2002. Interactive multiresolution hair modeling and editing. In Proc. of SIGGRAPH conference, ACM. Google ScholarDigital Library
    23. LAURENTINI, A. 1994. The visual hull concept for silhouette-based image understanding. IEEE Trans. on Pattern Analysis and Machine Intelligence 16, 2. Google ScholarDigital Library
    24. LU, R., KOENDERINK, J. J., AND KAPPERS, A. M. 1999. Specularities on surfaces with tangential hairs or grooves. In Proc. of the International Conference on Computer Vision, IEEE, 839–846.Google Scholar
    25. MARSCHNER, S. R., JENSEN, H. W., CAMMARANO, M., WORLEY, S., AND HANRAHAN, P. 2003. Light scattering from human hair fibers. ACM Trans. on Graphics 22, 3, 780–791. Google ScholarDigital Library
    26. MATUSIK, W., PFISTER, H., ZIEGLER, R., NGAN, A., AND MCMILLAN, L. 2002. Acquisition and rendering of transparent and refractive objects. In Proc. of the Eurographics Workshop on Rendering. Google ScholarDigital Library
    27. MEER, P., AND GEORGESCU, B. 2001. Edge detection with embedded confidence. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 12. Google ScholarDigital Library
    28. NAKAJIMA, M., MING, K. W., AND TAKASHI, H. 1998. Generation of 3D hair model from multiple pictures. In IEEE Computer Graphics & Applications (12) 1998, 183–169.Google Scholar
    29. PLANTE, E., CANI, M.-P., AND POULIN, P. 2001. A layered wisp model for simulating interactions inside long hair. In Proc. of Computer Animation and Simulation, Eurographics. Google ScholarDigital Library
    30. RUSHMEIER, H. E. 2001. 3D capture for computer graphics. In Third International Conference on 3D Digital Imaging and Modeling.Google ScholarCross Ref
    31. SHEN, J., AND CASTAN, S. 1986. An optimal linear operator for edge detection. In Proc. of Conference on Computer Vision and Pattern Recognition, IEEE.Google Scholar
    32. TOMASI, C., AND MANDUCHI, R. 1998. Bilateral filtering for gray and color images. In Proc. of the International Conference on Computer Vision, IEEE, 839–846. Google ScholarDigital Library
    33. TSCHUMPERLÉ, D., AND DERICHE, R. 2002. Orthonormal vector sets regularization with pde’s and applications. International Journal on Computer Vision 50 (12), 237–252. Google ScholarDigital Library
    34. WATSON, G. S. 1983. Statistics on spheres. John Wiley and Sons.Google Scholar
    35. YANG, R., M., P., AND WELCH, G. 2003. Dealing with textureless regions and specular highlights. In Proc. of the International Conference on Computer Vision, IEEE. Google ScholarDigital Library
    36. YITZHAKY, Y., AND PELI, E. 2003. A method for objective edge detection, evaluation and detector parameter selection. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 8. Google ScholarDigital Library
    37. ZIOU, D., AND TABBONE, S. 1998. Edge detection techniques – an overview. International Journal of Pattern Recognition and Image Analysis 8, 537–559.Google Scholar

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