“Robust hair capture using simulated examples” by Hu, Ma, Luo and Li

  • ©Liwen Hu, Chongyang Ma, Linjie Luo, and Hao Li




    Robust hair capture using simulated examples

Session/Category Title: Hair & Collisions




    We introduce a data-driven hair capture framework based on example strands generated through hair simulation. Our method can robustly reconstruct faithful 3D hair models from unprocessed input point clouds with large amounts of outliers. Current state-of-the-art techniques use geometrically-inspired heuristics to derive global hair strand structures, which can yield implausible hair strands for hairstyles involving large occlusions, multiple layers, or wisps of varying lengths. We address this problem using a voting-based fitting algorithm to discover structurally plausible configurations among the locally grown hair segments from a database of simulated examples. To generate these examples, we exhaustively sample the simulation configurations within the feasible parameter space constrained by the current input hairstyle. The number of necessary simulations can be further reduced by leveraging symmetry and constrained initial conditions. The final hairstyle can then be structurally represented by a limited number of examples. To handle constrained hairstyles such as a ponytail of which realistic simulations are more difficult, we allow the user to sketch a few strokes to generate strand examples through an intuitive interface. Our approach focuses on robustness and generality. Since our method is structurally plausible by construction, we ensure an improved control during hair digitization and avoid implausible hair synthesis for a wide range of hairstyles.


    1. Beeler, T., Bickel, B., Noris, G., Marschner, S., Beardsley, P., Sumner, R. W., and Gross, M. 2012. Coupled 3D reconstruction of sparse facial hair and skin. ACM Trans. Graph. 31, 117:1–117:10. Google ScholarDigital Library
    2. Bergou, M., Wardetzky, M., Robinson, S., Audoly, B., and Grinspun, E. 2008. Discrete elastic rods. ACM Trans. Graph. 27, 3, 63:1–63:12. Google ScholarDigital Library
    3. Bertails, F., Audoly, B., Querleux, B., Leroy, F., Leveque, J.-L., and Cani, M.-P. 2005. Predicting natural hair shapes by solving the statics of flexible rods. In Eurographics (short papers), 81–84.Google ScholarDigital Library
    4. Bertails, F., Audoly, B., Cani, M.-P., Querleux, B., Leroy, F., and Lévêque, J.-L. 2006. Super-helices for predicting the dynamics of natural hair. ACM Trans. Graph. 25, 3, 1180–1187. Google ScholarDigital Library
    5. Besl, P., and McKay, N. D. 1992. A method for registration of 3-d shapes. IEEE Trans. on PAMI 14, 2, 239–256. Google ScholarDigital Library
    6. Bradley, D., Nowrouzezahrai, D., and Beardsley, P. 2013. Image-based reconstruction and synthesis of dense foliage. ACM Trans. Graph. 32, 4, 74:1–74:10. Google ScholarDigital Library
    7. Chai, M., Wang, L., Weng, Y., Yu, Y., Guo, B., and Zhou, K. 2012. Single-view hair modeling for portrait manipulation. ACM Trans. Graph. 31, 4, 116:1–116:8. Google ScholarDigital Library
    8. Chai, M., Wang, L., Weng, Y., Jin, X., and Zhou, K. 2013. Dynamic hair manipulation in images and videos. ACM Trans. Graph. 32, 4, 75:1–75:8. Google ScholarDigital Library
    9. Choe, B., and Ko, H.-S. 2005. A statistical wisp model and pseudophysical approaches for interactive hairstyle generation. TVCG 11, 2, 160–170. Google ScholarDigital Library
    10. Comaniciu, D., and Meer, P. 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. on PAMI 24, 5, 603–619. Google ScholarDigital Library
    11. Daviet, G., Bertails-Descoubes, F., and Boissieux, L. 2011. A hybrid iterative solver for robustly capturing coulomb friction in hair dynamics. ACM Trans. Graph. 30, 6, 139:1–139:12. Google ScholarDigital Library
    12. Derouet-Jourdan, A., Bertails-Descoubes, F., and Thollot, J. 2013. Floating tangents for approximating spatial curves with G1 piecewise helices. Computer Aided Geometric Design 30, 5, 490–520. Google ScholarDigital Library
    13. Echevarria, J. I., Bradley, D., Gutierrez, D., and Beeler, T. 2014. Capturing and stylizing hair for 3D fabrication. ACM Trans. Graph. 33, 4. Google ScholarDigital Library
    14. Fischler, M. A., and Bolles, R. C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6, 381–395. Google ScholarDigital Library
    15. Fu, H., Wei, Y., Tai, C.-L., and Quan, L. 2007. Sketching hairstyles. In SBIM ’07, 31–36. Google ScholarDigital Library
    16. Furukawa, Y., and Ponce, J. 2010. Accurate, dense, and robust multiview stereopsis. IEEE Trans. PAMI 32, 1362–1376. Google ScholarDigital Library
    17. Hadap, S., and Magnenat-Thalmann, N. 2000. Interactive hair styler based on fluid flow. In Eurographics Workshop on Computer Animation and Simulation 2000, Springer, 87–99.Google Scholar
    18. Hadap, S., and Magnenat-Thalmann, N. 2001. Modeling dynamic hair as a continuum. Computer Graphics Forum 20, 3, 329–338.Google ScholarCross Ref
    19. Jakob, W., Moon, J. T., and Marschner, S. 2009. Capturing hair assemblies fiber by fiber. ACM Trans. Graph. 28, 5, 164:1–164:9. Google ScholarDigital Library
    20. Kim, T.-Y., and Neumann, U. 2002. Interactive multiresolution hair modeling and editing. ACM Trans. Graph. 21, 3, 620–629. Google ScholarDigital Library
    21. Lay Herrera, T., Zinke, A., and Weber, A. 2012. Lighting hair from the inside: A thermal approach to hair reconstruction. ACM Trans. Graph. 31, 6, 146:1–146:9. Google ScholarDigital Library
    22. Li, G., Liu, L., Zheng, H., and Mitra, N. J. 2010. Analysis, reconstruction and manipulation using arterial snakes. ACM Trans. Graph. 29, 5, 152:1–152:10. Google ScholarDigital Library
    23. Li, H., Yu, J., Ye, Y., and Bregler, C. 2013. Realtime facial animation with on-the-fly correctives. ACM Trans. Graph. 32, 4, 42:1–42:10. Google ScholarDigital Library
    24. Livny, Y., Yan, F., Olson, M., Chen, B., Zhang, H., and El-sana, J. 2010. Automatic reconstruction of tree skeletal structures from point clouds. ACM Trans. Graph. 29, 6, 151:1–151:8. Google ScholarDigital Library
    25. Luo, L., Li, H., Paris, S., Weise, T., Pauly, M., and Rusinkiewicz, S. 2012. Multi-view hair capture using orientation fields. In CVPR, 1490–1497. Google ScholarDigital Library
    26. Luo, L., Li, H., and Rusinkiewicz, S. 2013. Structure-aware hair capture. ACM Trans. Graph. 32, 4, 76:1–76:12. Google ScholarDigital Library
    27. Luo, L., Zhang, C., Zhang, Z., and Rusinkiewicz, S. 2013. Wide-baseline hair capture using strand-based refinement. In CVPR, 265–272. Google ScholarDigital Library
    28. Mitra, N. J., Guibas, L. J., and Pauly, M. 2006. Partial and approximate symmetry detection for 3D geometry. ACM Trans. Graph. 25, 3, 560–568. Google ScholarDigital Library
    29. Nan, L., Sharf, A., Zhang, H., Cohen-Or, D., and Chen, B. 2010. SmartBoxes for interactive urban reconstruction. ACM Trans. Graph. 29, 4, 93:1–93:10. Google ScholarDigital Library
    30. Paris, S., Briceño, H. M., and Sillion, F. X. 2004. Capture of hair geometry from multiple images. ACM Trans. Graph. 23, 3, 712–719. Google ScholarDigital Library
    31. Paris, S., Chang, W., Kozhushnyan, O. I., Jarosz, W., Matusik, W., Zwicker, M., and Durand, F. 2008. Hair photobooth: Geometric and photometric acquisition of real hairstyles. ACM Trans. Graph. 27, 3, 30:1–30:9. Google ScholarDigital Library
    32. Rother, C., Kolmogorov, V., and Blake, A. 2004. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 3, 309–314. Google ScholarDigital Library
    33. Selle, A., Lentine, M., and Fedkiw, R. 2008. A mass spring model for hair simulation. ACM Trans. Graph. 27, 3, 64:1–64:11. Google ScholarDigital Library
    34. Shao, T., Xu, W., Zhou, K., Wang, J., Li, D., and Guo, B. 2012. An interactive approach to semantic modeling of indoor scenes with an rgbd camera. ACM Trans. Graph. 31, 6, 136:1–136:11. Google ScholarDigital Library
    35. Shen, C.-H., Fu, H., Chen, K., and Hu, S.-M. 2012. Structure recovery by part assembly. ACM Trans. Graph. 31, 6, 180:1–180:11. Google ScholarDigital Library
    36. Wang, L., Yu, Y., Zhou, K., and Guo, B. 2009. Example-based hair geometry synthesis. ACM Trans. Graph. 28, 3, 56:1–56:9. Google ScholarDigital Library
    37. Ward, K., Lin, M. C., Lee, J., Fisher, S., and Macri, D. 2003. Modeling hair using level-of-detail representations. In Proc. CASA, 41–47. Google ScholarDigital Library
    38. Ward, K., Bertails, F., yong Kim, T., Marschner, S. R., paule Cani, M., and Lin, M. C. 2006. A survey on hair modeling: Styling, simulation, and rendering. TVCG 13, 2, 213–234. Google ScholarDigital Library
    39. Wei, Y., Ofek, E., Quan, L., and Shum, H.-Y. 2005. Modeling hair from multiple views. ACM Trans. Graph. 24, 3, 816–820. Google ScholarDigital Library
    40. Wither, J., Bertails, F., and Cani, M.-P. 2007. Realistic hair from a sketch. In SMI ’07, 33–42. Google ScholarDigital Library
    41. Xu, K., Zheng, H., Zhang, H., Cohen-Or, D., Liu, L., and Xiong, Y. 2011. Photo-inspired model-driven 3D object modeling. ACM Trans. Graph. 30, 4, 80:1–80:10. Google ScholarDigital Library
    42. Yu, Y. 2001. Modeling realistic virtual hairstyles. In Pacific Graphics’01, 295–304. Google ScholarDigital Library
    43. Yuksel, C., Schaefer, S., and Keyser, J. 2009. Hair meshes. ACM Trans. Graph. 28, 5, 166:1–166:7. Google ScholarDigital Library

ACM Digital Library Publication:

Overview Page: