“Practical level-of-detail aggregation of fur appearance” by Zhu, Zhao, Wang, Xu and Yan

  • ©Junqiu Zhu, Sizhe Zhao, Lu Wang, Yanning Xu, and Ling-Qi Yan




    Practical level-of-detail aggregation of fur appearance



    Fur appearance rendering is crucial for the realism of computer generated imagery, but is also a challenge in computer graphics for many years. Much effort has been made to accurately simulate the multiple-scattered light transport among fur fibers, but the computation cost is still very high, since the number of fur fibers is usually extremely large. In this paper, we aim at reducing the number of fur fibers while preserving realistic fur appearance. We present an aggregated fur appearance model, using one thick cylinder to accurately describe the aggregated optical behavior of a bunch of fur fibers, including the multiple scattering of light among them. Then, to acquire the parameters of our aggregated model, we use a lightweight neural network to map individual fur fiber’s optical properties to those in our aggregated model. Finally, we come up with a practical heuristic that guides the simplification process of fur dynamically at different bounces of the light, leading to a practical level-of-detail rendering scheme. Our method achieves nearly the same results as the ground truth, but performs 3.8×-13.5× faster.


    1. Matt Jen-Yuan Chiang, Benedikt Bitterli, Chuck Tappan, and Brent Burley. 2016. A practical and controllable hair and fur model for production path tracing. In Computer Graphics Forum, Vol. 35. Wiley Online Library, 275–283.Google Scholar
    2. Robert L Cook, John Halstead, Maxwell Planck, and David Ryu. 2007. Stochastic simplification of aggregate detail. ACM Transactions on Graphics (TOG) 26, 3 (2007), 79–es.Google ScholarDigital Library
    3. Thomas Davies, Derek Nowrouzezahrai, and Alec Jacobson. 2020. Overfit Neural Networks as a Compact Shape Representation. arXiv:2009.09808 [cs.GR]Google Scholar
    4. Eugene d’Eon, Guillaume Francois, Martin Hill, Joe Letteri, and Jean-Marie Aubry. 2011. An energy-conserving hair reflectance model. In Computer Graphics Forum, Vol. 30. Wiley Online Library, 1181–1187.Google Scholar
    5. Eugene d’Eon, Steve Marschner, and Johannes Hanika. 2013. Importance sampling for physically-based hair fiber models. In SIGGRAPH Asia 2013 Technical Briefs. 1–4.Google Scholar
    6. Jonathan Dupuy, Eric Heitz, Jean-Claude Iehl, Pierre Poulin, Fabrice Neyret, and Victor Ostromoukhov. 2013. Linear efficient antialiased displacement and reflectance mapping. ACM Trans. Graph. 32, 6 (2013), 1–11.Google ScholarDigital Library
    7. EA DICE. 2006. Frostbite Engine. https://www.ea.com/frostbiteGoogle Scholar
    8. Epic Games. 2019. Unreal Engine. https://www.unrealengine.comGoogle Scholar
    9. Charles Han, Bo Sun, Ravi Ramamoorthi, and Eitan Grinspun. 2007. Frequency domain normal map filtering. ACM Trans. Graph. 26, 3 (2007), 28.Google ScholarDigital Library
    10. Christophe Hery and Ravi Ramamoorthi. 2012. Importance sampling of reflection from hair fibers. Journal of Computer Graphics Techniques (JCGT) 1, 1 (2012), 1–17.Google Scholar
    11. Wenzel Jakob. 2010. Mitsuba renderer. http://www.mitsuba-renderer.org.Google Scholar
    12. Wenzel Jakob, Miloš Hašan, Ling-Qi Yan, Jason Lawrence, Ravi Ramamoorthi, and Steve Marschner. 2014. Discrete stochastic microfacet models. ACM Trans. Graph. 33, 4 (2014), 1–10.Google ScholarDigital Library
    13. Henrik Wann Jensen. 1996. Global illumination using photon maps. In Eurographics workshop on Rendering techniques. Springer, 21–30.Google ScholarCross Ref
    14. J. T. Kajiya and T. L. Kay. 1989. Rendering Fur with Three Dimensional Textures. In Proceedings of the 16th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’89). Association for Computing Machinery, New York, NY, USA, 271–280. Google ScholarDigital Library
    15. Nima Khademi Kalantari, Steve Bako, and Pradeep Sen. 2015. A Machine Learning Approach for Filtering Monte Carlo Noise. ACM Trans. Graph. 34, 4, Article 122 (July 2015), 12 pages.Google ScholarDigital Library
    16. Pramook Khungurn and Steve Marschner. 2017. Azimuthal Scattering from Elliptical Hair Fibers. ACM Trans. Graph. 36, 2, Article 13 (apr 2017), 23 pages. Google ScholarDigital Library
    17. Pramook Khungurn, Daniel Schroeder, Shuang Zhao, S Marschner, and K Bala. 2015. Matching micro-appearance models to real fabrics. ACM Trans. Graph 3, 10.1145 (2015), 2818648.Google Scholar
    18. Richard Lee and Carol O’Sullivan. 2007. Accelerated Light Propagation Through Participating Media.. In [email protected] Eurographics. 17–23.Google Scholar
    19. Stephen R Marschner, Henrik Wann Jensen, Mike Cammarano, Steve Worley, and Pat Hanrahan. 2003. Light scattering from human hair fibers. ACM Trans. Graph. 22, 3 (2003), 780–791.Google ScholarDigital Library
    20. Johannes Meng, Marios Papas, Ralf Habel, Carsten Dachsbacher, Steve Marschner, Markus H Gross, and Wojciech Jarosz. 2015. Multi-scale modeling and rendering of granular materials. ACM Trans. Graph. 34, 4 (2015), 49–1.Google ScholarDigital Library
    21. Jonathan T Moon and Stephen R Marschner. 2006. Simulating multiple scattering in hair using a photon mapping approach. ACM Trans. Graph. 25, 3 (2006), 1067–1074.Google ScholarDigital Library
    22. J. T. Moon, B. Walter, and S. Marschner. 2008. Efficient multiple scattering in hair using spherical harmonics. ACM Transactions on Graphics 27, 3 (2008), 1–7.Google ScholarDigital Library
    23. Jonathan T Moon, Bruce Walter, and Stephen R Marschner. 2007. Rendering discrete random media using precomputed scattering solutions. In Proceedings of the 18th Eurographics conference on Rendering Techniques. 231–242.Google ScholarDigital Library
    24. Thomas Müller, Marios Papas, Markus Gross, Wojciech Jarosz, and Jan Novák. 2016. Efficient rendering of heterogeneous polydisperse granular media. ACM Trans. Graph. 35, 6 (2016), 1–14.Google ScholarDigital Library
    25. Thomas Müller, Fabrice Rousselle, Jan Novák, and Alexander Keller. 2021. Real-time neural radiance caching for path tracing. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1–16.Google ScholarDigital Library
    26. Marc Olano and Dan Baker. 2010. LEAN Mapping. In Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (Washington, D.C.) (I3D ’10). Association for Computing Machinery, New York, NY, USA, 181–188. Google ScholarDigital Library
    27. Peiran Ren, Yue Dong, Stephen Lin, Xin Tong, and Baining Guo. 2015. Image based relighting using neural networks. ACM Trans. Graph. 34, 4 (2015), 1–12.Google ScholarDigital Library
    28. Peiran Ren, Jiaping Wang, Minmin Gong, Stephen Lin, Xin Tong, and Baining Guo. 2013. Global illumination with radiance regression functions. ACM Trans. Graph. 32, 4 (2013), 1–12.Google ScholarDigital Library
    29. Aaron A Sandel. 2013. Brief communication: Hair density and body mass in mammals and the evolution of human hairlessness. American journal of physical anthropology 152, 1 (2013), 145–150.Google Scholar
    30. TuringBot Software. 2020. TuringBot. https://turingbotsoftware.com.Google Scholar
    31. Delio Vicini, Wenzel Jakob, and Anton Kaplanyan. 2021. A Non-Exponential Transmittance Model for Volumetric Scene Representations. ACM Trans. Graph. 40, 4, Article 136 (jul 2021), 16 pages. Google ScholarDigital Library
    32. Lifan Wu, Shuang Zhao, Ling-Qi Yan, and Ravi Ramamoorthi. 2019. Accurate appearance preserving prefiltering for rendering displacement-mapped surfaces. ACM Trans. Graph. 38, 4 (2019), 1–14.Google ScholarDigital Library
    33. Mengqi Xia, Bruce Walter, Eric Michielssen, David Bindel, and Steve Marschner. 2020. A wave optics based fiber scattering model. ACM Transactions on Graphics (TOG) 39, 6 (2020), 1–16.Google ScholarDigital Library
    34. Ling-Qi Yan, Miloš Hašan, Wenzel Jakob, Jason Lawrence, Steve Marschner, and Ravi Ramamoorthi. 2014. Rendering Glints on High-Resolution Normal-Mapped Specular Surfaces. ACM Trans. Graph. 33, 4 (2014), 1–9.Google ScholarDigital Library
    35. Ling-Qi Yan, Miloš Hašan, Steve Marschner, and Ravi Ramamoorthi. 2016. Positionnormal distributions for efficient rendering of specular microstructure. ACM Trans. Graph. 35, 4 (2016), 56.Google ScholarDigital Library
    36. Ling-Qi Yan, H. W. Jensen, and R. Ramamoorthi. 2017a. An efficient and practical near and far field fur reflectance model. ACM Trans. Graph. 36, 4 (2017), 1–13.Google ScholarDigital Library
    37. Ling-Qi Yan, Weilun Sun, Henrik Wann Jensen, and Ravi Ramamoorthi. 2017b. A BSSRDF Model for Efficient Rendering of Fur with Global Illumination. ACM Trans. Graph. 36, 6, Article 208 (Nov. 2017), 13 pages. Google ScholarDigital Library
    38. Ling-Qi Yan, Chi-Wei Tseng, Henrik Wann Jensen, and Ravi Ramamoorthi. 2015. Physically-accurate fur reflectance: Modeling, measurement and rendering. ACM Trans. Graph. 34, 6 (2015), 1–13.Google ScholarDigital Library
    39. Shuang Zhao, Lifan Wu, Frédo Durand, and Ravi Ramamoorthi. 2016. Downsampling scattering parameters for rendering anisotropic media. ACM Trans. Graph. 35, 6 (2016), 1–11.Google ScholarDigital Library
    40. Junqiu Zhu, Yaoyi Bai, Zilin Xu, Steve Bako, Edgar Velázquez-Armendáriz, Lu Wang, Pradeep Sen, Miloš Hašan, and Ling-Qi Yan. 2021. Neural complex luminaires: representation and rendering. ACM Trans. Graph. 40, 4 (2021), 1–12.Google ScholarDigital Library
    41. Arno Zinke and Andreas Weber. 2007. Light scattering from filaments. IEEE Transactions on Visualization and Computer Graphics 13, 2 (2007), 342–356.Google ScholarDigital Library
    42. Arno Zinke, Cem Yuksel, Andreas Weber, and John Keyser. 2008. Dual Scattering Approximation for Fast Multiple Scattering in Hair. ACM Trans. Graph. 27, 3 (aug 2008), 1–10. Google ScholarDigital Library

ACM Digital Library Publication:

Overview Page: