# RigNet: Neural Rigging for Articulated Characters

arXiv: https://arxiv.org/abs/2005.00559 · HTML: https://mlsanity.com/paper/2005.00559/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 30 of 32)

- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [NeRF-Editing: Geometry Editing of Neural Radiance Fields](https://mlsanity.com/paper/2205.04978/) (2022)
- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [TextDeformer: Geometry Manipulation using Text Guidance](https://mlsanity.com/paper/2304.13348/) (2023)
- [Reconstructing Animatable Categories from Videos](https://mlsanity.com/paper/2305.06351/) (2023)
- [Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks](https://mlsanity.com/paper/2205.01355/) (2022)
- [Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes](https://mlsanity.com/paper/2205.02904/) (2022)
- [iTACO: Interactable Digital Twins of Articulated Objects from Casually Captured RGBD Videos](https://mlsanity.com/paper/2506.08334/) (2025)
- [AniClipart: Clipart Animation with Text-to-Video Priors](https://mlsanity.com/paper/2404.12347/) (2024)
- [SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters](https://mlsanity.com/paper/2203.04746/) (2022)
- [Zero-shot Pose Transfer for Unrigged Stylized 3D Characters](https://mlsanity.com/paper/2306.00200/) (2023)
- [GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis](https://mlsanity.com/paper/2402.16994/) (2024)
- [HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset](https://mlsanity.com/paper/2412.02317/) (2024)
- [Transfer Learning for Pose Estimation of Illustrated Characters](https://mlsanity.com/paper/2108.01819/) (2021)
- [Learning to Infer Kinematic Hierarchies for Novel Object Instances](https://mlsanity.com/paper/2110.07911/) (2021)
- [CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular Videos](https://mlsanity.com/paper/2304.06937/) (2023)
- [Robust Biharmonic Skinning Using Geometric Fields](https://mlsanity.com/paper/2406.00238/) (2024)
- [SMF: Template-free and Rig-free Animation Transfer using Kinetic Codes](https://mlsanity.com/paper/2504.04831/) (2025)
- [PoissonNet: A Local-Global Approach for Learning on Surfaces](https://mlsanity.com/paper/2510.14146/) (2025)
- [AniGen: Unified S3 Fields for Animatable 3D Asset Generation](https://mlsanity.com/paper/2604.08746/) (2026)
- [Controllable Shape Modeling with Neural Generalized Cylinder](https://mlsanity.com/paper/2410.03675/) (2024)
- [Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos](https://mlsanity.com/paper/2107.11756/) (2021)
- [CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network](https://mlsanity.com/paper/2305.18808/) (2023)
- [ARC-Flow : Articulated, Resolution-Agnostic, Correspondence-Free Matching and Interpolation of 3D Shapes Under Flow Fields](https://mlsanity.com/paper/2503.02606/) (2025)
- [CageNet: A Meta-Framework for Learning on Wild Meshes](https://mlsanity.com/paper/2505.18772/) (2025)
- [SketchRodGS: Sketch-based Extraction of Slender Geometries for Animating Gaussian Splatting Scenes](https://mlsanity.com/paper/2601.02072/) (2026)
- [TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animation](https://mlsanity.com/paper/2606.12153/) (2026)
- [Bunraku: Turning a Single Illustration into an Editable Live2D Character](https://mlsanity.com/paper/2607.27348/) (2026)
- [Temporal Parameter-free Deep Skinning of Animated Meshes](https://mlsanity.com/paper/2109.07249/) (2021)
- [Empowering Children to Create AI-Enabled Augmented Reality Experiences](https://mlsanity.com/paper/2508.08467/) (2025)

## Abstract (arXiv)

We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. The architecture is trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. Our evaluation is three-fold: we show better results than prior art when quantitatively compared to animator rigs; qualitatively we show that our rigs can be expressively posed and animated at multiple levels of detail; and finally, we evaluate the impact of various algorithm choices on our output rigs.