@article{rignet2020,
  title = {RigNet: Neural Rigging for Articulated Characters},
  author = {Xu, Z. and Zhou, Y. and Kalogerakis, E. and Landreth, C. and Singh, K.},
  year = {2020},
  journal = {ACM Transactions on Graphics (SIGGRAPH 2020)},
  url = {https://arxiv.org/abs/2005.00559},
  abstract = {Xu, Zhou, Kalogerakis, Landreth and Singh (2020) presented RigNet, the first end-to-end learned method for automatic character rigging that predicts skeleton joint positions, connectivity and skin weights directly from a 3D mesh input without class-specific templates or priors. RigNet uses a graph neural network operating on the mesh surface to jointly predict where joints should be placed and how vertices should be weighted to each joint. The approach generalises across humanoid, stylised and creature meshes, producing rigs usable in standard animation software. RigNet was published at SIGGRAPH 2020, spawned Blender community add-ons, and became the named baseline for all subsequent automatic-rigging research, filling a previously empty pipeline stage in the collection.},
  keywords = {character-animation, 3d-generation},
  note = {AI \& Animation Education Knowledge Base}
}