Fetching the paper…
Reading the bibliography…
Today's Mixed Reality head-mounted displays track the user's head pose in world space as well as the user's hands for interaction in both Augmented Reality and Virtual Reality scenarios.
Moré, J.J.: The levenberg-marquardt algorithm: implementation and theory. In: Numerical analysis, pp. 105–116. Springer (1978)
1978
Earlier work this paper cites.
Luenberger, D.G., Ye, Y., et al.: Linear and nonlinear programming, vol. 2. Springer (1984)
1984
Earlier work this paper cites.
Goldenberg, A., Benhabib, B., Fenton, R.: A complete generalized solution to the inverse kinematics of robots. IEEE Journal on Robotics and Automation 1
1985
Earlier work this paper cites.
Parker, J.K., Khoogar, A.R., Goldberg, D.E.: Inverse kinematics of redundant robots using genetic algorithms. In: 1989 IEEE International Conference on Robotics and Automation. pp. 271–272. IEEE Computer Society (1989)
1989
Earlier work this paper cites.
Wang, L.C., Chen, C.C.: A combined optimization method for solving the inverse kinematics problems of mechanical manipulators. IEEE Transactions on Robotics and Automation 7
1991
Earlier work this paper cites.
Zhao, J., Badler, N.I.: Inverse kinematics positioning using nonlinear programming for highly articulated figures. ACM Transactions on Graphics (TOG) 13
1994
Earlier work this paper cites.
Troje, N.F.: Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. Journal of vision 2
2002
Earlier work this paper cites.
CMU MoCap Dataset. http://mocap.cs.cmu.edu/ (2004)
2004
Earlier work this paper cites.
Grochow, K., Martin, S.L., Hertzmann, A., Popović, Z.: Style-based inverse kinematics. In: ACM SIGGRAPH 2004 Papers, pp. 522–531 (2004)
2004
Earlier work this paper cites.
Sumner, R.W., Zwicker, M., Gotsman, C., Popović, J.: Mesh-based inverse kinematics. ACM transactions on graphics (TOG) 24
2005
Earlier work this paper cites.
Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation mocap database hdm05. Tech. Rep. CG-2007-2, Universität Bonn (June 2007)
2007
Earlier work this paper cites.
Aristidou, A., Lasenby, J.: Fabrik: A fast, iterative solver for the inverse kinematics problem. Graphical Models 73
2011
Earlier work this paper cites.
Bócsi, B., Nguyen-Tuong, D., Csató, L., Schoelkopf, B., Peters, J.: Learning inverse kinematics with structured prediction. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 698–703. IEEE (2011)
2011
Earlier work this paper cites.
Çavdar, T., Mohammad, M., Milani, R.A.: A new heuristic approach for inverse kinematics of robot arms. Advanced Science Letters 19
2013
Earlier work this paper cites.
Duka, A.V.: Neural network based inverse kinematics solution for trajectory tracking of a robotic arm. Procedia Technology 12
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: Smpl: A skinned multi-person linear model. ACM transactions on graphics (TOG) 34
2015
Earlier work this paper cites.
Rokbani, N., Casals, A., Alimi, A.M.: Ik-fa, a new heuristic inverse kinematics solver using firefly algorithm. In: Computational intelligence applications in modeling and control, pp. 369–395. Springer (2015)
2015
Earlier work this paper cites.
Csiszar, A., Eilers, J., Verl, A.: On solving the inverse kinematics problem using neural networks. In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). pp. 1–6. IEEE (2017)
2017
Earlier work this paper cites.
Heidicker, P., Langbehn, E., Steinicke, F.: Influence of avatar appearance on presence in social vr. In: 2017 IEEE Symposium on 3D User Interfaces (3DUI). pp. 233–234 (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Von Marcard, T., Rosenhahn, B., Black, M.J., Pons-Moll, G.: Sparse inertial poser: Automatic 3d human pose estimation from sparse imus. In: Computer graphics forum. vol. 36, pp. 349–360. Wiley Online Library (2017)
2017
Cited alongside, same era.
RootMotion Final IK. https://assetstore.unity.com/packages/tools/animation/final-ik-14290 (2018)
2018
Cited alongside, same era.
Huang, Y., Kaufmann, M., Aksan, E., Black, M.J., Hilliges, O., Pons-Moll, G.: Deep inertial poser: Learning to reconstruct human pose from sparse inertial measurements in real time. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) 37
2018
Cited alongside, same era.
Parger, M., Mueller, J.H., Schmalstieg, D., Steinberger, M.: Human upper-body inverse kinematics for increased embodiment in consumer-grade virtual reality. In: Proceedings of the 24th ACM symposium on virtual reality software and technology. pp. 1–10 (2018)
2018
Cited alongside, same era.
Fan, H., Xiong, B., Mangalam, K., Li, Y., Yan, Z., Malik, J., Feichtenhofer, C.: Multiscale vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6824–6835 (2021)
2021
Later among the works it cites.
Kang, M., Cho, Y., Yoon, S.E.: Rcik: Real-time collision-free inverse kinematics using a collision-cost prediction network. IEEE Robotics and Automation Letters 7
2021
Later among the works it cites.
Li, J., Xu, C., Chen, Z., Bian, S., Yang, L., Lu, C.: Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3383–3393 (2021)
2021
Later among the works it cites.
Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1833–1844 (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ruppel, P., Hendrich, N., Starke, S., Zhang, J.: Cost functions to specify full-body motion and multi-goal manipulation tasks. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). pp. 3152–3159. IEEE (2018)
2018
Cited alongside, same era.
Villegas, R., Yang, J., Ceylan, D., Lee, H.: Neural kinematic networks for unsupervised motion retargetting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8639–8648 (2018)
2018
Cited alongside, same era.
Waltemate, T., Gall, D., Roth, D., Botsch, M., Latoschik, M.E.: The impact of avatar personalization and immersion on virtual body ownership, presence, and emotional response. IEEE Transactions on Visualization and Computer Graphics 24
2018
Cited alongside, same era.
Dai, Z., Yang, Z., Yang, Y., Carbonell, J.G., Le, Q., Salakhutdinov, R.: Transformer-xl: Attentive language models beyond a fixed-length context. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 2978–2988 (2019)
2019
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. Annual Conference of the North American Chapter of the Association for Computational Linguistics (2019)
2019
Cited alongside, same era.
Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: AMASS: Archive of motion capture as surface shapes. In: International Conference on Computer Vision. pp. 5442–5451 (Oct 2019)
2019
Cited alongside, same era.
Starke, S., Zhang, H., Komura, T., Saito, J.: Neural state machine for character-scene interactions. ACM Trans. Graph. 38
2019
Cited alongside, same era.
Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5745–5753 (2019)
2019
Cited alongside, same era.
Lin, K., Wang, L., Liu, Z.: End-to-end human pose and mesh reconstruction with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1954–1963 (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)
2021
Later among the works it cites.
Marić, F., Giamou, M., Hall, A.W., Khoubyarian, S., Petrović, I., Kelly, J.: Riemannian optimization for distance-geometric inverse kinematics. IEEE Transactions on Robotics 38
2021
Later among the works it cites.
Sun, Z., Cao, S., Yang, Y., Kitani, K.M.: Rethinking transformer-based set prediction for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3611–3620 (2021)
2021
Later among the works it cites.
Wang, J., Liu, L., Xu, W., Sarkar, K., Theobalt, C.: Estimating egocentric 3d human pose in global space. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11500–11509 (2021)
2021
Later among the works it cites.
Yang, D., Kim, D., Lee, S.H.: Lobstr: Real-time lower-body pose prediction from sparse upper-body tracking signals. In: Computer Graphics Forum. vol. 40, pp. 265–275. Wiley Online Library (2021)
2021
Later among the works it cites.
Yi, X., Zhou, Y., Xu, F.: Transpose: real-time 3d human translation and pose estimation with six inertial sensors. ACM Transactions on Graphics (TOG) 40
2021
Later among the works it cites.
Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., Ding, Z.: 3d human pose estimation with spatial and temporal transformers. Proceedings of the IEEE International Conference on Computer Vision (2021)
2021
Later among the works it cites.
Ames, B., Morgan, J.: Ikflow: Generating diverse inverse kinematics solutions. IEEE Robotics and Automation Letters (2022)
2022
Closest in time.
Li, W., Liu, H., Tang, H., Wang, P., Van Gool, L.: Mhformer: Multi-hypothesis transformer for 3d human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13147–13156 (2022)
2022
Closest in time.
Meinhardt, T., Kirillov, A., Leal-Taixe, L., Feichtenhofer, C.: Trackformer: Multi-object tracking with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8844–8854 (2022)
2022
Closest in time.
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17683–17693 (2022)
2022
Closest in time.
Yi, X., Zhou, Y., Habermann, M., Shimada, S., Golyanik, V., Theobalt, C., Xu, F.: Physical inertial poser (pip): Physics-aware real-time human motion tracking from sparse inertial sensors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13167–13178 (2022)
2022
Closest in time.
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5728–5739 (2022)
2022
Closest in time.
Zhang, X., Bhatnagar, B.L., Guzov, V., Starke, S., Pons-Moll, G.: Couch: Towards controllable human-chair interactions. In: European Conference on Computer Vision). Springer (October 2022)
2022
Closest in time.
Zhao, Z., Wu, Z., Zhang, Y., Li, B., Jia, J.: Tracking objects as pixel-wise distributions. In: Proceedings of the European Conference on Computer Vision (2022)
2022
Closest in time.