[1] Sven Kreiss, Lorenzo Bertoni, and Alexandre Alahi. Openpifpaf: Composite fields for semantic keypoint detection and spatio-temporal association. IEEE Transactions on Intelligent Transportation Systems, 23(8): 13498-13511, 2021.
[2] Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo, Joonseok Lee, Sookyung Kim, and Edward Choi. Vid-ode: Continuous-time video generation with neural ordinary differential equation. In AAAI Conference on Artificial Intelligence (AAAI), volume 35, pages 2412–2422, 2021.
[3] Vikramjit Sidhu, Edgar Tretschk, Vladislav Golyanik, Antonio Agudo, and Christian Theobalt. Neural dense non-rigid structure from motion with latent space constraints. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XVI 16, pages 204-222. Springer, 2020.
[4] Hui Deng, Tong Zhang, Yuchao Dai, Jiawei Shi, Yiran Zhong, and Hongdong Li. Deep non-rigid structure-from-motion: A sequence-to-sequence translation perspective. arXiv preprint arXiv:2204.04730, 2022.
[5] Qiang Fu, Hongshan Yu, Xiaolong Wang, Zhengeng Yang, Yong He, Hong Zhang, and Ajmal Mian. Fast orb-slam without keypoint descriptors. IEEE Transactions on Image Processing, 31:1433-1446, 2021.
[6] Ravi Garg, Anastasios Roussos, and Lourdes Agapito. A variational approach to video registration with subspace constraints. International Journal of Computer Vision (IJCV), 104:286-314, 2013.
[7] Wenbin Li, Darren Cosker, and Matthew Brown. Drift robust non-rigid optical flow enhancement for long sequences. Journal of Intelligent & Fuzzy Systems, 31(5):2583-2595, 2016.
[8] Carl Doersch, Ankush Gupta, Larisa Markeeva, Adria Recasens Continente, Kucas Smaira, Yusuf Aytar, Joao Carreira, Andrew Zisserman, and Yi Yang. Tap-vid: A benchmark for tracking any point in a video. In NeurIPS Datasets Track, 2022.
[9] Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision (ECCV), pages 59-75. Springer, 2022.
[10] Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. Advances in Neural Information Processing Systems (NeurIPS), 31, 2018.
[11] Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, et al. Kubric: A scalable dataset generator. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3749-3761, 2022.
[12] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European Conference on Computer Vision (ECCV), pages 402-419. Springer, 2020.
[13] Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li. Flowformer: A transformer architecture for optical flow. In European Conference on Computer Vision (ECCV), pages 668-685. Springer, 2022.
[14] Berthold KP Horn and Brian G Schunck. Determining optical flow. Artificial intelligence, 17(1-3):185-203, 1981.
[15] Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, and Cordelia Schmid. Epicflow: Edge-preserving interpolation of correspondences for optical flow. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1164-1172, 2015.
[16] Qifeng Chen and Vladlen Koltun. Full flow: Optical flow estimation by global optimization over regular grids. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4706-4714, 2016.
[17] Christian Bailer, Bertram Taetz, and Didier Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. In IEEE International Conference on Computer Vision (ICCV), pages 4015-4023, 2015.
[18] Thomas Brox and Jitendra Malik. Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(3): 500-513, 2010.