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Human hands possess the dexterity to interact with diverse objects such as grasping specific parts of the objects and/or approaching them from desired directions.
2015
Earlier work this paper cites.
Romero, J., Tzionas, D., Black, M.J.: Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia) (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Hwangbo, J., Lee, J., Hutter, M.: Per-contact iteration method for solving contact dynamics. Robotics and Automation Letters (RA-L) (2018)
2018
Earlier work this paper cites.
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., Levine, S.: Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. In: Robotics: Science and Systems (RSS) (2018)
2018
Earlier work this paper cites.
Christen, S., Stevšić, S., Hilliges, O.: Demonstration-guided deep reinforcement learning of control policies for dexterous human-robot interaction. In: International Conference on Robotics and Automation (ICRA) (2019)
2019
Earlier work this paper cites.
Tekin, B., Bogo, F., Pollefeys, M.: H+O: Unified egocentric recognition of 3D hand-object poses and interactions. In: Computer Vision and Pattern Recognition (CVPR). pp. 4511–4520 (2019)
2019
Earlier work this paper cites.
Corona, E., Pumarola, A., Alenyà, G., Moreno-Noguer, F., Rogez, G.: GanHand: Predicting human grasp affordances in multi-object scenes. In: Computer Vision and Pattern Recognition (CVPR). pp. 5030–5040 (2020)
2020
Earlier work this paper cites.
Eppner, C., Mousavian, A., Fox, D.: ACRONYM: A large-scale grasp dataset based on simulation. In: International Conference on Robotics and Automation (ICRA) (2020)
2020
Earlier work this paper cites.
Hampali, S., Rad, M., Oberweger, M., Lepetit, V.: HOnnotate: A method for 3d annotation of hand and object poses. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Earlier work this paper cites.
Hampali, S., Rad, M., Oberweger, M., Lepetit, V.: Honnotate: A method for 3d annotation of hand and object poses. In: CVPR (2020)
2020
Earlier work this paper cites.
Li, S., Jiang, J., Ruppel, P., Liang, H., Ma, X., Hendrich, N., Sun, F., Zhang, J.: A mobile robot hand-arm teleoperation system by vision and imu. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 10900–10906. IEEE (2020)
2020
Earlier work this paper cites.
Taheri, O., Ghorbani, N., Black, M.J., Tzionas, D.: GRAB: A dataset of whole-body human grasping of objects. In: European Conference on Computer Vision (ECCV). vol. 12349, pp. 581–600 (2020)
2020
Earlier work this paper cites.
Cao, Z., Radosavovic, I., Kanazawa, A., Malik, J.: Reconstructing hand-object interactions in the wild. In: International Conference on Computer Vision (ICCV). pp. 12417–12426 (2021)
2021
Earlier work this paper cites.
Chao, Y.W., Yang, W., Xiang, Y., Molchanov, P., Handa, A., Tremblay, J., Narang, Y.S., Van Wyk, K., Iqbal, U., Birchfield, S., Kautz, J., Fox, D.: DexYCB: A benchmark for capturing hand grasping of objects. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Earlier work this paper cites.
Fan, Z., Spurr, A., Kocabas, M., Tang, S., Black, M., Hilliges, O.: Learning to disambiguate strongly interacting hands via probabilistic per-pixel part segmentation. In: International Conference on 3D Vision (3DV) (2021)
2021
Earlier work this paper cites.
Jiang, H., Liu, S., Wang, J., Wang, X.: Hand-object contact consistency reasoning for human grasps generation. In: International Conference on Computer Vision (ICCV) (2021)
2021
Earlier work this paper cites.
Liu, S., Jiang, H., Xu, J., Liu, S., Wang, X.: Semi-supervised 3D hand-object poses estimation with interactions in time. In: Computer Vision and Pattern Recognition (CVPR). pp. 14687–14697 (2021)
2021
Cited alongside, same era.
Mandikal, P., Grauman, K.: DexVIP: Learning dexterous grasping with human hand pose priors from video. In: Conference on Robot Learning (CoRL) (2021)
2021
Cited alongside, same era.
Mandikal, P., Grauman, K.: Learning dexterous grasping with object-centric visual affordances. In: International Conference on Robotics and Automation (ICRA) (2021)
2021
Cited alongside, same era.
Yang, L., Zhan, X., Li, K., Xu, W., Li, J., Lu, C.: CPF: Learning a contact potential field to model the hand-object interaction. In: International Conference on Computer Vision (ICCV) (2021)
2021
Cited alongside, same era.
Liu, Q., Cui, Y., Ye, Q., Sun, Z., Li, H., Li, G., Shao, L., Chen, J.: Dexrepnet: Learning dexterous robotic grasping network with geometric and spatial hand-object representations. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 3153–3160. IEEE (2023)
2023
Later among the works it cites.
Qin, Y., Huang, B., Yin, Z.H., Su, H., Wang, X.: DexPoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation. In: Conference on Robot Learning (CoRL) (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Turpin, D., Zhong, T., Zhang, S., Zhu, G., Heiden, E., Macklin, M., Tsogkas, S., Dickinson, S., Garg, A.: Fast-grasp’d: Dexterous multi-finger grasp generation through differentiable simulation. In: International Conference on Robotics and Automation (ICRA) (2023)
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2021
Cited alongside, same era.
Chen, Y., Wu, T., Wang, S., Feng, X., Jiang, J., Lu, Z., McAleer, S., Dong, H., Zhu, S.C., Yang, Y.: Towards human-level bimanual dexterous manipulation with reinforcement learning. Adv. Neural Inform. Process. Syst. (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Christen, S., Kocabas, M., Aksan, E., Hwangbo, J., Song, J., Hilliges, O.: D-Grasp: Physically plausible dynamic grasp synthesis for hand-object interactions. In: Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: Text-to-3d using 2d diffusion. arXiv (2022)
2022
Cited alongside, same era.
Qin, Y., Wu, Y.H., Liu, S., Jiang, H., Yang, R., Fu, Y., Wang, X.: DexMV: Imitation learning for dexterous manipulation from human videos. In: European Conference on Computer Vision (ECCV) (2022)
2022
Cited alongside, same era.
Taheri, O., Choutas, V., Black, M.J., Tzionas, D.: GOAL: Generating 4D whole-body motion for hand-object grasping. In: Computer Vision and Pattern Recognition (CVPR) (2022), https://goal.is.tue.mpg.de
2022
Cited alongside, same era.
2023
Later among the works it cites.
Wan, W., Geng, H., Liu, Y., Shan, Z., Yang, Y., Yi, L., Wang, H.: UniDexGrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning. In: International Conference on Computer Vision (ICCV) (2023)
2023
Later among the works it cites.
Xu, Y., Wan, W., Zhang, J., Liu, H., Shan, Z., Shen, H., Wang, R., Geng, H., Weng, Y., Chen, J., et al.: UniDexGrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy. In: Computer Vision and Pattern Recognition (CVPR) (2023)
2023
Later among the works it cites.
Ye, J., Wang, J., Huang, B., Qin, Y., Wang, X.: Learning continuous grasping function with a dexterous hand from human demonstrations. Robotics and Automation Letters (RA-L) (2023)
2023
Later among the works it cites.
Ye, Y., Li, X., Gupta, A., Mello, S.D., Birchfield, S., Song, J., Tulsiani, S., Liu, S.: Affordance diffusion: Synthesizing hand-object interactions. In: Computer Vision and Pattern Recognition (CVPR) (2023)
2023
Later among the works it cites.
Ze, Y., Liu, Y., Shi, R., Qin, J., Yuan, Z., Wang, J., Xu, H.: H-index: Visual reinforcement learning with hand-informed representations for dexterous manipulation. Conference on Neural Information Processing Systems (NeurIPS) (2023)
2023
Later among the works it cites.
Zheng, J., Zheng, Q., Fang, L., Liu, Y., Yi, L.: CAMS: Canonicalized manipulation spaces for category-level functional hand-object manipulation synthesis. In: Computer Vision and Pattern Recognition (CVPR) (2023)
2023
Later among the works it cites.
Braun, J., Christen, S., Kocabas, M., Aksan, E., Hilliges, O.: Physically plausible full-body hand-object interaction synthesis. In: International Conference on 3D Vision (3DV) (2024)
2024
Closest in time.
Christen, S., Feng, L., Yang, W., Chao, Y.W., Hilliges, O., Song, J.: Synh2r: Synthesizing hand-object motions for learning human-to-robot handovers. In: IEEE International Conference on Robotics and Automation (ICRA) (2024)
2024
Closest in time.
Duran, E., Kocabas, M., Choutas, V., Fan, Z., Black, M.J.: HMP: Hand motion priors for pose and shape estimation from video. In: Winter Conference on Applications of Computer Vision (WACV). pp. 6353–6363 (January 2024)
2024
Closest in time.
Fan, Z., Ohkawa, T., Yang, L., Lin, N., Zhou, Z., Zhou, S., Liang, J., Gao, Z., Zhang, X., Zhang, X., Li, F., Zheng, L., Lu, F., Zeid, K.A., Leibe, B., On, J., Baek, S., Prakash, A., Gupta, S., He, K., Sato, Y., Hilliges, O., Chang, H.J., Yao, A.: Benchmarks and challenges in pose estimation for egocentric hand interactions with objects. In: European Conference on Computer Vision (ECCV) (2024)
2024
Closest in time.
Fan, Z., Parelli, M., Kadoglou, M.E., Kocabas, M., Chen, X., Black, M.J., Hilliges, O.: HOLD: Category-agnostic 3d reconstruction of interacting hands and objects from video (2024)
2024
Closest in time.
Zhang, H., Christen, S., Fan, Z., Zheng, L., Hwangbo, J., Song, J., Hilliges, O.: ArtiGrasp: Physically plausible synthesis of bi-manual dexterous grasping and articulation. In: International Conference on 3D Vision (3DV) (2024)
2024
Closest in time.