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We propose a novel test-time optimization approach for efficiently and robustly tracking any pixel at any time in a video.
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Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8934–8943 (2018)
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Xian, K., Shen, C., Cao, Z., Lu, H., Xiao, Y., Li, R., Luo, Z.: Monocular relative depth perception with web stereo data supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
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Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2566–2576 (2019)
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Liu, L., Gu, J., Lin, K.Z., Chua, T.S., Theobalt, C.: Neural sparse voxel fields. NeurIPS (2020)
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Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: ECCV (2020)
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Teed, Z., Deng, J.: Raft: Recurrent all-pairs field transforms for optical flow. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. pp. 402–419. Springer (2020)
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Jiang, S., Campbell, D., Lu, Y., Li, H., Hartley, R.: Learning to estimate hidden motions with global motion aggregation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9772–9781 (2021)
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Lee, A.X., Devin, C.M., Zhou, Y., Lampe, T., Bousmalis, K., Springenberg, J.T., Byravan, A., Abdolmaleki, A., Gileadi, N., Khosid, D., et al.: Beyond pick-and-place: Tackling robotic stacking of diverse shapes. In: 5th Annual Conference on Robot Learning (2021)
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Xu, H., Zhang, J., Cai, J., Rezatofighi, H., Tao, D.: Gmflow: Learning optical flow via global matching. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8121–8130 (2022)
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Ye, V., Li, Z., Tucker, R., Kanazawa, A., Snavely, N.: Deformable sprites for unsupervised video decomposition. In: CVPR. pp. 2657–2666 (2022)
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2023
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Cited alongside, same era.
Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-nerf: Neural radiance fields for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10318–10327 (2021)
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Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: Loftr: Detector-free local feature matching with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8922–8931 (2021)
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Xu, H., Yang, J., Cai, J., Zhang, J., Tong, X.: High-resolution optical flow from 1d attention and correlation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10498–10507 (2021)
2021
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Zhang, F., Woodford, O.J., Prisacariu, V.A., Torr, P.H.: Separable flow: Learning motion cost volumes for optical flow estimation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 10807–10817 (2021)
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Bian, Z., Jabri, A., Efros, A.A., Owens, A.: Learning pixel trajectories with multiscale contrastive random walks. In: CVPR. pp. 6508–6519 (2022)
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Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: Tensorf: Tensorial radiance fields. In: European Conference on Computer Vision. pp. 333–350. Springer (2022)
2022
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Doersch, C., Gupta, A., Markeeva, L., Recasens, A., Smaira, L., Aytar, Y., Carreira, J., Zisserman, A., Yang, Y.: Tap-vid: A benchmark for tracking any point in a video. Advances in Neural Information Processing Systems 35
2022
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Harley, A.W., Fang, Z., Fragkiadaki, K.: Particle video revisited: Tracking through occlusions using point trajectories. In: European Conference on Computer Vision. pp. 59–75. Springer (2022)
2022
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2023
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Guizilini, V., Vasiljevic, I., Chen, D., Ambru
2023
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2023
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Li, W.: Superglue-based deep learning method for image matching from multiple viewpoints. In: Proceedings of the 2023 8th International Conference on Mathematics and Artificial Intelligence. pp. 53–58 (2023)
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Zheng, Y., Harley, A.W., Shen, B., Wetzstein, G., Guibas, L.J.: Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19855–19865 (2023)
2023
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Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R.C., Schindler, K.: Repurposing diffusion-based image generators for monocular depth estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
2024
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Neoral, M., Šerỳch, J., Matas, J.: Mft: Long-term tracking of every pixel. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 6837–6847 (2024)
2024
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Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: Unleashing the power of large-scale unlabeled data. In: CVPR (2024)
2024
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