Fetching the paper…
Reading the bibliography…
We introduce a new hair modeling method that uses a dual representation of classical hair strands and 3D Gaussians to produce accurate and realistic strand-based reconstructions from multi-view data.
Woo, M., Neider, J., Davis, T., Shreiner, D.: OpenGL programming guide: the official guide to learning OpenGL, version 1.2. Addison-Wesley Longman Publishing Co., Inc. (1999)
1999
Earlier work this paper cites.
Paris, S., Briceño, H.M., Sillion, F.X.: Capture of hair geometry from multiple images. ACM SIGGRAPH 2004 Papers (2004)
2004
Earlier work this paper cites.
Paris, S., Chang, W., Kozhushnyan, O.I., Jarosz, W., Matusik, W., Zwicker, M., Durand, F.: Hair photobooth: geometric and photometric acquisition of real hairstyles. ACM Transactions on Graphics 27
2008
Earlier work this paper cites.
Yuksel, C., Schaefer, S., Keyser, J.: Hair meshes. ACM Transactions on Graphics 28
2009
Earlier work this paper cites.
Piuze, E., Kry, P.G., Siddiqi, K.: Generalized helicoids for modeling hair geometry. In: Computer Graphics Forum. vol. 30, pp. 247–256. Wiley Online Library (2011)
2011
Earlier work this paper cites.
Luo, L., Li, H., Paris, S., Weise, T., Pauly, M., Rusinkiewicz, S.: Multi-view hair capture using orientation fields. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1490–1497. IEEE (2012)
2012
Earlier work this paper cites.
Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K.: Facewarehouse: A 3d facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics 20
2013
Earlier work this paper cites.
Luo, L., Li, H., Rusinkiewicz, S.: Structure-aware hair capture. ACM Transactions on Graphics 32
2013
Earlier work this paper cites.
Luo, L., Zhang, C., Zhang, Z., Rusinkiewicz, S.: Wide-baseline hair capture using strand-based refinement. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 265–272 (2013)
2013
Earlier work this paper cites.
Chai, M., Luo, L., Sunkavalli, K., Carr, N., Hadap, S., Zhou, K.: High-quality hair modeling from a single portrait photo. ACM Transactions on Graphics 34
2015
Earlier work this paper cites.
Chai, M., Shao, T., Wu, H., Weng, Y., Zhou, K.: Autohair: fully automatic hair modeling from a single image. ACM Trans. Graph. 35
2016
Earlier work this paper cites.
Chiang, M.J.Y., Bitterli, B., Tappan, C., Burley, B.: A practical and controllable hair and fur model for production path tracing. In: Computer Graphics Forum. vol. 35, pp. 275–283. Wiley Online Library (2016)
2016
Earlier work this paper cites.
Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Fei, Y., Maia, H.T., Batty, C., Zheng, C., Grinspun, E.: A multi-scale model for simulating liquid-hair interactions. ACM Transactions on Graphics (TOG) 36
2017
Earlier work this paper cites.
Li, T., Bolkart, T., Black, M.J., Li, H., Romero, J.: Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) 36
2017
Earlier work this paper cites.
Zhang, M., Chai, M., Wu, H., Yang, H., Zhou, K.: A data-driven approach to four-view image-based hair modeling. ACM Transactions on Graphics 36
2017
Earlier work this paper cites.
Fascione, L., Hanika, J., Pieké, R., Villemin, R., Hery, C., Gamito, M., Emrose, L., Mazzone, A.: Path tracing in production. In: ACM SIGGRAPH 2018 Courses, pp. 1–79 (2018)
2018
Earlier work this paper cites.
Zhang, M., Wu, P., Wu, H., Weng, Y., Zheng, Y., Zhou, K.: Modeling hair from an rgb-d camera. ACM Transactions on Graphics 37
2018
Earlier work this paper cites.
Nam, G., Wu, C., Kim, M.H., Sheikh, Y.: Strand-accurate multi-view hair capture. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 155–164 (2019)
2019
Cited alongside, same era.
Xing, J., Nagano, K., Chen, W., Xu, H., Wei, L.y., Zhao, Y., Lu, J., Kim, B., Li, H.: Hairbrush for immersive data-driven hair modeling. In: Proceedings of the 32Nd Annual ACM Symposium on User Interface Software and Technology. pp. 263–279 (2019)
2019
Cited alongside, same era.
Shen, Y., Zhang, C., Fu, H., Zhou, K., Zheng, Y.: Deepsketchhair: Deep sketch-based 3d hair modeling. IEEE transactions on visualization and computer graphics 27
2020
Cited alongside, same era.
Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., Zhang, Y.: Blindly assess image quality in the wild guided by a self-adaptive hyper network. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3664–3673 (2020)
2020
Cited alongside, same era.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
2023
Later among the works it cites.
Kirschstein, T., Qian, S., Giebenhain, S., Walter, T., Nießner, M.: Nersemble: Multi-view radiance field reconstruction of human heads. ACM Trans. Graph. 42
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: Bundle-adjusting neural radiance fields. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 5721–5731 (2021)
2021
Cited alongside, same era.
Lombardi, S., Simon, T., Schwartz, G., Zollhoefer, M., Sheikh, Y., Saragih, J.: Mixture of volumetric primitives for efficient neural rendering. ACM Transactions on Graphics (ToG) 40
2021
Cited alongside, same era.
Lombardi, S., Simon, T., Schwartz, G., Zollhoefer, M., Sheikh, Y., Saragih, J.M.: Mixture of volumetric primitives for efficient neural rendering. ACM Transactions on Graphics (TOG) 40
2021
Cited alongside, same era.
Wang, Z., Nam, G., Stuyck, T., Lombardi, S., Zollhoefer, M., Hodgins, J.K., Lassner, C.: Hvh: Learning a hybrid neural volumetric representation for dynamic hair performance capture. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 6133–6144 (2021)
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. In: Advances in Neural Information Processing Systems (NeurIPS) (2022)
2022
Cited alongside, same era.
Rosu, R.A., Saito, S., Wang, Z., Wu, C., Behnke, S., Nam, G.: Neural strands: Learning hair geometry and appearance from multi-view images. In: European Conference on Computer Vision (2022)
2022
Cited alongside, same era.
Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In: Advances in Neural Information Processing Systems (NeurIPS) (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Shen, Y., Saito, S., Wang, Z., Maury, O., Wu, C., Hodgins, J., Zheng, Y., Nam, G.: Ct2hair: High-fidelity 3d hair modeling using computed tomography. ACM Transactions on Graphics 42
2023
Later among the works it cites.
Sklyarova, V., Chelishev, J., Dogaru, A., Medvedev, I., Lempitsky, V., Zakharov, E.: Neural haircut: Prior-guided strand-based hair reconstruction. In: Proceedings of IEEE International Conference on Computer Vision (ICCV) (2023)
2023
Later among the works it cites.
Sklyarova, V., Zakharov, E., Hilliges, O., Black, M.J., Thies, J.: Haar: Text-conditioned generative model of 3d strand-based human hairstyles. ArXiv (Dec 2023)
2023
Later among the works it cites.
Wang, Z., Nam, G., Stuyck, T., Lombardi, S., Cao, C., Saragih, J.M., Zollhoefer, M., Hodgins, J.K., Lassner, C.: Neuwigs: A neural dynamic model for volumetric hair capture and animation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8641–8651 (June 2023)
2023
Later among the works it cites.
Xiang, J., Gao, X., Guo, Y., Zhang, J.: Flashavatar: High-fidelity digital avatar rendering at 300fps (2023)
2023
Later among the works it cites.
Xu, Y., Chen, B., Li, Z., Zhang, H., Wang, L., Zheng, Z., Liu, Y.: Gaussian head avatar: Ultra high-fidelity head avatar via dynamic gaussians (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zheng, Y., Jin, Z., Li, M., Huang, H., Ma, C., Cui, S., Han, X.: Hairstep: Transfer synthetic to real using strand and depth maps for single-view 3d hair modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12726–12735 (2023)
2023
Later among the works it cites.
Zhou, Y., Chai, M., Pepe, A., Gross, M., Beeler, T.: Groomgen: A high-quality generative hair model using hierarchical latent representations. ACM Transactions on Graphics (TOG) 42
2023
Later among the works it cites.
Zielonka, W., Bolkart, T., Thies, J.: Instant volumetric head avatars. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4574–4584 (2023)
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.