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CANRIG: Cross-Attention Neural Face Rigging with Variable Local Control
Arad Mohammadi, Sebastian Weiss, Jakob Buhmann, Loic Ciccone, Robert Sumner, Derek Bradley, Martin Guay
Cross-attention neural network for face rigging enabling variable local control over expression and pose for production face pipelines.
Abstract
CANRig is a fully automated neural facial rigging method that lets artists deform a 3D face mesh by manipulating sparse control handles with continuous, user-defined locality at runtime. It formulates deformation as a cross-attention between control handles and mesh vertices, where the attention matrix is masked by per-vertex influence weights derived from a user-specified radius and falloff, enabling smooth transitions from precise local edits to broad global changes. A shape-preserving workflow built from zero-preserving MLPs, an added base-shape attention column, and a shape-preserving loss guarantees that previous edits are maintained during iterative, non-destructive editing and yields zero-error inversion of captured performances. For high-resolution live-action meshes the method predicts local patch blendshape weights instead of per-vertex deltas, and the authors demonstrate posing, clip editing, dialogue replacement, and expression transfer across both feature animation and visual effects pipelines.
How to read this
- Category
- Method: neural face rigging with variable local control
- Contributions
- CANRig, a fully automated neural facial rig that deforms a face mesh via sparse control handles with continuous, user-defined locality at runtime
- A cross-attention formulation between handles and vertices, masked by per-vertex influence weights from a user-specified radius and falloff, spanning local to global edits
- A shape-preserving workflow (zero-preserving MLPs, base-shape attention column, shape-preserving loss) giving non-destructive iterative editing and zero-error inversion of captured performances, with local patch-blendshape prediction for high-res live-action meshes
- Context
- Continues neural rig-approximation work such as Accurate Face Rig Approximation with Deep Differential Subspace Reconstruction (Song et al. 2020), adding artist-controllable locality via attention.Builds on: Accurate Face Rig Approximation with Deep Differential Subspace Reconstruction
- Correctness
- Capabilities (posing, clip editing, dialogue replacement, expression transfer) are demonstrated across feature-animation and VFX pipelines; the zero-error inversion and shape preservation are architectural guarantees, so the practical question is generalization and handle-placement sensitivity rather than reconstruction error.
- Clarity
- Dense but well-structured; a first pass conveys the handle-and-locality idea, a second pass needed for the masked cross-attention and shape-preserving components.
- How to read it
- Read first for how locality is expressed via the masked attention weights; second pass on the zero-preserving MLPs and shape-preserving loss if you need the non-destructive-editing guarantees.
Built upon by
Nothing yet.
Related work
- Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild 2023 / SIGGRAPH
- Sketch-Based Controllers for Blendshape Facial Animation 2015 / Eurographics
- Example-Based Facial Rigging 2010 / SIGGRAPH
- Reusable Facial Rigging and Animation: Create Once, Use Many 2007 / PhD Thesis
Keywords
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