{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T10:25:16Z","timestamp":1789467916673,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032373137","type":"print"},{"value":"9783032373144","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-37314-4_12","type":"book-chapter","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:52:08Z","timestamp":1789462328000},"page":"210-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MorphGS: Morphology-Adaptive Articulated 3D Motion Transfer from\u00a0Videos"],"prefix":"10.1007","author":[{"given":"Taeyeon","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngju","family":"Na","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jumin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhyuk","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Eui","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"issue":"4","key":"12_CR1","doi-asserted-by":"publisher","first-page":"62:1","DOI":"10.1145\/3386569.3392462","volume":"39","author":"K Aberman","year":"2020","unstructured":"Aberman, K., Li, P., Lischinski, D., Sorkine-Hornung, O., Cohen-Or, D., Chen, B.: Skeleton-aware networks for deep motion retargeting. ACM Trans. Graph. (TOG) 39(4), 62:1 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"12_CR2","unstructured":"Adobe: Mixamo. https:\/\/www.mixamo.com. Accessed 4 Mar 2026"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Aygun, M., Mac Aodha, O.: Saor: single-view articulated object reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10382\u201310391 (2024)","DOI":"10.1109\/CVPR52733.2024.00988"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Baran, I., Popovi\u0107, J.: Automatic rigging and animation of 3d characters. ACM Trans. Graph. (TOG) 26(3), 72\u2013es (2007)","DOI":"10.1145\/1276377.1276467"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Beker, D., Kato, H., Morariu, M.A., Ando, T., Matsuoka, T., Kehl, W., Gaidon, A.: Monocular differentiable rendering for self-supervised 3D object detection. In: European Conference on Computer Vision, pp. 514\u2013529. Springer (2020)","DOI":"10.1007\/978-3-030-58589-1_31"},{"key":"12_CR6","unstructured":"Carion, N., Gustafson, L., Hu, Y.T., Debnath, S., Hu, R., Suris, D., Ryali, C., Alwala, K.V., Khedr, H., Huang, A., et\u00a0al.: Sam 3: Segment Anything with Concepts. arXiv preprint arXiv:2511.16719 (2025)"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, C., Lee, G.H.: Weakly-supervised 3D pose transfer with keypoints. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15156\u201315165 (2023)","DOI":"10.1109\/ICCV51070.2023.01391"},{"key":"12_CR8","unstructured":"Fu, Z., Wei, J., Shen, W., Song, C., Yang, X., Liu, F., Yang, X., Lin, G.: Sync4D: Video Guided Controllable Dynamics for Physics-based 4D Generation. arXiv preprint arXiv:2405.16849 (2024)"},{"issue":"6","key":"12_CR9","first-page":"1","volume":"37","author":"L Gao","year":"2018","unstructured":"Gao, L., Yang, J., Qiao, Y.L., Lai, Y.K., Rosin, P.L., Xu, W., Xia, S.: Automatic unpaired shape deformation transfer. ACM Trans. Graph. (ToG) 37(6), 1\u201315 (2018)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Gleicher, M.: Retargetting motion to new characters. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 33\u201342 (1998)","DOI":"10.1145\/280814.280820"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Goel, S., Pavlakos, G., Rajasegaran, J., Kanazawa, A., Malik, J.: Humans in 4D: reconstructing and tracking humans with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14783\u201314794 (2023)","DOI":"10.1109\/ICCV51070.2023.01358"},{"key":"12_CR12","unstructured":"Gong, K., Wen, Z., He, W., Xu, M., Wang, Q., Zhang, N., Li, Z., Lian, D., Zhao, W., He, X., et\u00a0al.: Mocapanything: unified 3D motion capture for arbitrary skeletons from monocular videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7089\u20137099 (2026)"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Hu, L., Zhang, H., Zhang, Y., Zhou, B., Liu, B., Zhang, S., Nie, L.: Gaussianavatar: towards realistic human avatar modeling from a single video via animatable 3D gaussians. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 634\u2013644 (2024)","DOI":"10.1109\/CVPR52733.2024.00067"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Huang, Y.H., Sun, Y.T., Yang, Z., Lyu, X., Cao, Y.P., Qi, X.: SC-GS: sparse-controlled gaussian splatting for editable dynamic scenes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4220\u20134230 (2024)","DOI":"10.1109\/CVPR52733.2024.00404"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Jakab, T., Li, R., Wu, S., Rupprecht, C., Vedaldi, A.: Farm3D: learning articulated 3D animals by distilling 2D diffusion. In: 2024 International Conference on 3D Vision (3DV), pp. 852\u2013861. IEEE (2024)","DOI":"10.1109\/3DV62453.2024.00051"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. In: Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00744"},{"issue":"4","key":"12_CR17","doi-asserted-by":"publisher","first-page":"139-1","DOI":"10.1145\/3592433","volume":"42","author":"B Kerbl","year":"2023","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4), 139\u20131 (2023)","journal-title":"ACM Trans. Graph."},{"key":"12_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2014). arXiv preprint arXiv:1412.6980"},{"issue":"20","key":"12_CR19","doi-asserted-by":"publisher","first-page":"4603","DOI":"10.3390\/s19204603","volume":"19","author":"O Kovalenko","year":"2019","unstructured":"Kovalenko, O., Golyanik, V., Malik, J., Elhayek, A., Stricker, D.: Structure from articulated motion: accurate and stable monocular 3D reconstruction without training data. Sensors 19(20), 4603 (2019)","journal-title":"Sensors"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Krull, A., Brachmann, E., Michel, F., Yang, M.Y., Gumhold, S., Rother, C.: Learning analysis-by-synthesis for 6D pose estimation in RGB-D images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 954\u2013962 (2015)","DOI":"10.1109\/ICCV.2015.115"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Takehara, H., Taketomi, T., Zheng, B., Nie\u00dfner, M.: 4dcomplete: non-rigid motion estimation beyond the observable surface. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12706\u201312716 (2021)","DOI":"10.1109\/ICCV48922.2021.01247"},{"key":"12_CR22","doi-asserted-by":"publisher","first-page":"21377","DOI":"10.52202\/079017-0674","volume":"37","author":"Z Li","year":"2024","unstructured":"Li, Z., Chen, Y., Liu, P.: Dreammesh4D: video-to-4D generation with sparse-controlled gaussian-mesh hybrid representation. Adv. Neural. Inf. Process. Syst. 37, 21377\u201321400 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Li, Z., Litvak, D., Li, R., Zhang, Y., Jakab, T., Rupprecht, C., Wu, S., Vedaldi, A., Wu, J.: Learning the 3D fauna of the web. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9752\u20139762 (2024)","DOI":"10.1109\/CVPR52733.2024.00931"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Liao, Z., Yang, J., Saito, J., Pons-Moll, G., Zhou, Y.: Skeleton-free pose transfer for stylized 3D characters. In: European Conference on Computer Vision, pp. 640\u2013656. Springer (2022)","DOI":"10.1007\/978-3-031-20086-1_37"},{"key":"12_CR25","unstructured":"Liu, J., Qu, Y., Yan, Q., Zeng, X., Wang, L., Liao, R.: Fr\u00e9chet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos. arXiv preprint arXiv:2407.16124 (2024)"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 34(6), 248:1\u2013248:16 (2015)","DOI":"10.1145\/2816795.2818013"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Lyu, J., Zhu, T., Gu, Y., Lin, L., Cheng, P., Liu, Y., Tang, X., An, L.: Animer: Animal Pose and Shape Estimation Using Family Aware Transformer (2024). arXiv preprint arXiv:2412.00837","DOI":"10.1109\/CVPR52734.2025.01629"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Maheshwari, S., Narain, R., Hebbalaguppe, R.: Transfer4D: a framework for frugal motion capture and deformation transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12836\u201312846 (2023)","DOI":"10.1109\/CVPR52729.2023.01234"},{"issue":"1","key":"12_CR29","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Muralikrishnan, S., Dutt, N., Chaudhuri, S., Aigerman, N., Kim, V., Fisher, M., Mitra, N.J.: Temporal residual Jacobians for rig-free motion transfer. In: European Conference on Computer Vision, pp. 93\u2013109. Springer (2024)","DOI":"10.1007\/978-3-031-73636-0_6"},{"issue":"6","key":"12_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3763309","volume":"44","author":"S Muralikrishnan","year":"2025","unstructured":"Muralikrishnan, S., Dutt, N.S., Mitra, N.J.: SMF: template-free and rig-free animation transfer using kinetic codes. ACM Trans. Graph. (TOG) 44(6), 1\u201311 (2025)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van\u00a0Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 724\u2013732 (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"12_CR33","unstructured":"Ravi, N., Reizenstein, J., Novotny, D., Gordon, T., Lo, W.Y., Johnson, J., Gkioxari, G.: Accelerating 3D Deep Learning with Pytorch3D. arXiv:2007.08501 (2020)"},{"key":"12_CR34","doi-asserted-by":"crossref","unstructured":"Rueegg, N., Zuffi, S., Schindler, K., Black, M.J.: BARC: Learning to regress 3D dog shape from images by exploiting breed information. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3876\u20133884 (2022)","DOI":"10.1109\/CVPR52688.2022.00385"},{"key":"12_CR35","doi-asserted-by":"crossref","unstructured":"R\u00fcegg, N., Tripathi, S., Schindler, K., Black, M.J., Zuffi, S.: Bite: beyond priors for improved three-d dog pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8867\u20138876 (2023)","DOI":"10.1109\/CVPR52729.2023.00856"},{"key":"12_CR36","doi-asserted-by":"publisher","unstructured":"Sabathier, R., Mitra, N.J., Novotny, D.: Animal avatars: reconstructing animatable 3D animals from casual videos. In: Computer Vision\u2014ECCV 2024: 18th European Conference, Milan, Italy, September 29\u2013October 4, 2024, Proceedings, Part LXXIX, pp. 270\u2013287 (2024). https:\/\/doi.org\/10.1007\/978-3-031-72986-7_16","DOI":"10.1007\/978-3-031-72986-7_16"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Shtedritski, A., Rupprecht, C., Vedaldi, A.: SHIC: shape-image correspondences with no keypoint supervision. In: ECCV (2024)","DOI":"10.1007\/978-3-031-72913-3_8"},{"key":"12_CR38","doi-asserted-by":"crossref","unstructured":"Song, C., Li, X., Yang, F., Xu, Z., Wei, J., Liu, F., Feng, J., Lin, G., Zhang, J.: Puppeteer: Rig and Animate Your 3D Models. arXiv preprint arXiv:2508.10898 (2025)","DOI":"10.52202\/085713-2422"},{"key":"12_CR39","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"12_CR40","doi-asserted-by":"crossref","unstructured":"Villegas, R., Ceylan, D., Hertzmann, A., Yang, J., Saito, J.: Contact-aware retargeting of skinned motion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9720\u20139729 (2021)","DOI":"10.1109\/ICCV48922.2021.00958"},{"key":"12_CR41","doi-asserted-by":"crossref","unstructured":"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\u20138648 (2018)","DOI":"10.1109\/CVPR.2018.00901"},{"key":"12_CR42","doi-asserted-by":"crossref","unstructured":"Wang, A., Ma, W., Yuille, A., Kortylewski, A.: Neural textured deformable meshes for robust analysis-by-synthesis. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3108\u20133117 (2024)","DOI":"10.1109\/WACV57701.2024.00308"},{"key":"12_CR43","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, X., Liu, S., De\u00a0Mello, S., Gallo, O., Wang, X., Kautz, J.: Zero-shot pose transfer for unrigged stylized 3D characters. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8704\u20138714 (2023)","DOI":"10.1109\/CVPR52729.2023.00841"},{"key":"12_CR44","doi-asserted-by":"crossref","unstructured":"Wang, J., Wen, C., Fu, Y., Lin, H., Zou, T., Xue, X., Zhang, Y.: Neural pose transfer by spatially adaptive instance normalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5831\u20135839 (2020)","DOI":"10.1109\/CVPR42600.2020.00587"},{"key":"12_CR45","unstructured":"Wang, Y., Guo, C., Mu, Y., Javed, M.G., Zuo, X., Lu, J., Jiang, H., Cheng, L.: Motiondreamer: One-to-Many Motion Synthesis with Localized Generative Masked Transformer. arXiv preprint arXiv:2504.08959 (2025)"},{"key":"12_CR46","doi-asserted-by":"crossref","unstructured":"Wu, G., Yi, T., Fang, J., Xie, L., Zhang, X., Wei, W., Liu, W., Tian, Q., Wang, X.: 4D gaussian splatting for real-time dynamic scene rendering. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20310\u201320320 (2024)","DOI":"10.1109\/CVPR52733.2024.01920"},{"issue":"10","key":"12_CR47","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1007\/s11263-023-01819-5","volume":"131","author":"S Wu","year":"2023","unstructured":"Wu, S., Jakab, T., Rupprecht, C., Vedaldi, A.: Dove: learning deformable 3d objects by watching videos. Int. J. Comput. Vision 131(10), 2623\u20132634 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"12_CR48","doi-asserted-by":"crossref","unstructured":"Wu, S., Li, R., Jakab, T., Rupprecht, C., Vedaldi, A.: Magicpony: Learning articulated 3d animals in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8792\u20138802 (2023)","DOI":"10.1109\/CVPR52729.2023.00849"},{"key":"12_CR49","doi-asserted-by":"crossref","unstructured":"Wu, Z., Yu, C., Jiang, Y., Cao, C., Wang, F., Bai, X.: Sc4D: Sparse-controlled video-to-4D generation and motion transfer. In: European Conference on Computer Vision, pp. 361\u2013379. Springer (2024)","DOI":"10.1007\/978-3-031-72624-8_21"},{"key":"12_CR50","doi-asserted-by":"crossref","unstructured":"Xiao, J., Chai, J.X., Kanade, T.: A closed-form solution to non-rigid shape and motion recovery. In: European Conference on Computer Vision, pp. 573\u2013587. Springer (2004)","DOI":"10.1007\/978-3-540-24673-2_46"},{"key":"12_CR51","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zhu, W., Wu, W., Qian, C., Zhou, Q., Zhou, B., Loy, C.C.: Transmomo: invariance-driven unsupervised video motion retargeting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5306\u20135315 (2020)","DOI":"10.1109\/CVPR42600.2020.00535"},{"key":"12_CR52","doi-asserted-by":"publisher","first-page":"15296","DOI":"10.52202\/068431-1113","volume":"35","author":"CH Yao","year":"2022","unstructured":"Yao, C.H., Hung, W.C., Li, Y., Rubinstein, M., Yang, M.H., Jampani, V.: Lassie: learning articulated shapes from sparse image ensemble via 3D part discovery. Adv. Neural. Inf. Process. Syst. 35, 15296\u201315308 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"12_CR53","doi-asserted-by":"crossref","unstructured":"Yao, C.H., Xie, Y., Voleti, V., Jiang, H., Jampani, V.: Sv4D 2.0: enhancing spatio-temporal consistency in multi-view video diffusion for high-quality 4d generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13248\u201313258 (2025)","DOI":"10.1109\/ICCV51701.2025.01231"},{"key":"12_CR54","doi-asserted-by":"crossref","unstructured":"Yao, Y., Deng, Z., Hou, J.: Riggs: rigging of 3D gaussians for modeling articulated objects in videos. In: The IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2025)","DOI":"10.1109\/CVPR52734.2025.00525"},{"key":"12_CR55","doi-asserted-by":"crossref","unstructured":"Yoo, S., Koo, J., Yeo, K., Sung, M.: Neural Pose Representation Learning for Generating and Transferring Non-rigid Object Poses (2024). arXiv preprint arXiv:2406.09728","DOI":"10.52202\/079017-1083"},{"key":"12_CR56","unstructured":"Zhang, H., Chang, D., Li, F., Soleymani, M., Ahuja, N.: Magicpose4D: Crafting Articulated Models with Appearance and Motion Control (2024). arXiv preprint arXiv:2405.14017"},{"key":"12_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, H., Tian, Y., Zhou, X., Ouyang, W., Liu, Y., Wang, L., Sun, Z.: PyMAF: 3D human pose and shape regression with pyramidal mesh alignment feedback loop. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11446\u201311456 (2021)","DOI":"10.1109\/ICCV48922.2021.01125"},{"key":"12_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, J.P., Pu, C.F., Guo, M.H., Cao, Y.P., Hu, S.M.: One Model to Rig Them All: Diverse Skeleton Rigging With Unirig (2025). arXiv preprint arXiv:2504.12451","DOI":"10.1145\/3730930"},{"key":"12_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, J., Herrmann, C., Hur, J., Chen, E., Jampani, V., Sun, D., Yang, M.H.: Telling left from right: identifying geometry-aware semantic correspondence. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3076\u20133085 (2024)","DOI":"10.1109\/CVPR52733.2024.00297"},{"key":"12_CR60","doi-asserted-by":"crossref","unstructured":"Zhao, B.N., Wu, J., Wu, S.: Web-scale collection of video data for 4D animal reconstruction. Adv. Neural Inf. Process. Syst. 38 (2026)","DOI":"10.52202\/085713-0719"},{"key":"12_CR61","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/978-3-030-58542-6_21","volume-title":"Computer Vision\u2014ECCV 2020","author":"K Zhou","year":"2020","unstructured":"Zhou, K., Bhatnagar, B.L., Pons-Moll, G.: Unsupervised shape and pose disentanglement for 3D meshes. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12367, pp. 341\u2013357. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58542-6_21"},{"key":"12_CR62","doi-asserted-by":"crossref","unstructured":"Zuffi, S., Kanazawa, A., Jacobs, D.W., Black, M.J.: 3D menagerie: modeling the 3D shape and pose of animals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6365\u20136373 (2017)","DOI":"10.1109\/CVPR.2017.586"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37314-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:52:39Z","timestamp":1789462359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37314-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032373137","9783032373144"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37314-4_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"16 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}