TARig: Adaptive template-aware neural rigging for humanoid characters

Poirier, 2009, Rig retargeting for 3D animation, 103 Pantuwong, 2012, A novel template-based automatic rigging algorithm for articulated-character animation, Comput Animat Virtual Worlds, 23, 125, 10.1002/cav.1429 Baran, 2007, Automatic rigging and animation of 3d characters, ACM Trans Graph, 26, 72, 10.1145/1276377.1276467 Pascucci, 2007, Robust on-line computation of Reeb graphs: Simplicity and speed, 58 Pantuwong, 2010, Skeleton-growing: A vector-field-based 3D curve-skeleton extraction algorithm, 1 Xu, 2019, Predicting animation skeletons for 3d articulated models via volumetric nets, 298 Xu, 2020 Xu Z, Zhou Y, Yi L, Kalogerakis E. Morig: Motion-aware rigging of character meshes from point clouds. In: SIGGRAPH Asia 2022 conference papers. 2022, p. 1–9. Dionne O, de Lasa M. Geodesic voxel binding for production character meshes. In: Proceedings of the 12th ACM SIGGRAPH/eurographics symposium on computer animation. 2013, p. 173–80. Liu, 2019, Neuroskinning: Automatic skin binding for production characters with deep graph networks, ACM Trans Graph, 38, 1, 10.1145/3306346.3323045 AutoDesk, 2022 Adobe, 2022 Kavan L, Collins S, Žára J, O’Sullivan C. Skinning with dual quaternions. In: Proceedings of the 2007 symposium on interactive 3D graphics and games. 2007, p. 39–46. Kavan, 2008, Geometric skinning with approximate dual quaternion blending, ACM Trans Graph, 27, 1, 10.1145/1409625.1409627 Jacobson, 2011, Bounded biharmonic weights for real-time deformation, ACM Trans Graph, 30, 78, 10.1145/2010324.1964973 Kavan, 2012, Elasticity-inspired deformers for character articulation, ACM Trans Graph, 31, 1, 10.1145/2366145.2366215 Dionne, 2014, Geodesic binding for degenerate character geometry using sparse voxelization, IEEE Trans Vis Comput Graphics, 20, 1367, 10.1109/TVCG.2014.2321563 Wang, 2015, Linear subspace design for real-time shape deformation, ACM Trans Graph, 34, 1 Le, 2019, Direct delta mush skinning and variants, ACM Trans Graph, 38, 10.1145/3306346.3322982 Pan, 2021, HeterSkinNet: A heterogeneous network for skin weights prediction Bronstein, 2017, Geometric deep learning: going beyond euclidean data, IEEE Signal Process Mag, 34, 18, 10.1109/MSP.2017.2693418 Wu, 2020, A comprehensive survey on graph neural networks, IEEE Trans Neural Netw Learn Syst, 32, 4, 10.1109/TNNLS.2020.2978386 Zhang, 2020, Deep learning on graphs: A survey, IEEE Trans Knowl Data Eng Zhou, 2020, Graph neural networks: A review of methods and applications, AI Open, 1, 57, 10.1016/j.aiopen.2021.01.001 Boscaini, 2016, Learning shape correspondence with anisotropic convolutional neural networks, Adv Neural Inf Process Syst, 29 Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM. Geometric deep learning on graphs and manifolds using mixture model cnns. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 5115–24. Yi L, Su H, Guo X, Guibas LJ. Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 2282–90. Hanocka, 2019, Meshcnn: A network with an edge, ACM Trans Graph, 38, 1, 10.1145/3306346.3322959 Masci J, Boscaini D, Bronstein M, Vandergheynst P. Geodesic convolutional neural networks on riemannian manifolds. In: Proceedings of the IEEE international conference on computer vision workshops. 2015, p. 37–45. Verma N, Boyer E, Verbeek J. Feastnet: Feature-steered graph convolutions for 3d shape analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 2598–606. Wang, 2019, Dynamic graph cnn for learning on point clouds, Acm Trans Grap (Tog), 38, 1, 10.1145/3326362 Sudre, 2017, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations, 240 Paszke, 2019, Pytorch: An imperative style, high-performance deep learning library, Adv Neural Inf Process Syst, 32 Fey, 2019 Thekumparampil, 2018