PIS: Pose-Interpolation Smoothness for Skinning Weight Refinement

Modern automatic-rigging predictors (UniRig, Make-It-Animatable, RigNet) place a skeleton on a 3D mesh in seconds, but the resulting skinning weights animate poorly. Linear-blend skinning (LBS) is non-linear in pose space, so noisy weights amplify into visible candy-wrapper artifacts---the surface twisting and contracting at joint bends---and volume collapse. ARAP-style remedies penalize these symptoms one pose at a time and cannot reach the cross-pose curvature that produces them. We propose \emph{Pose-Interpolation Smoothness} (PIS), a self-supervised, training-free, test-time refiner: minimize the per-vertex gap between the deformation at the SO(3) midpoint of two random poses and the linear average of the two endpoint deformations. The midpoint is obtained by orthogonally projecting the arithmetic mean of the two rotations back onto the rotation group, sidestepping the matrix-logarithm gradient that breaks down near identity and antipodal poses. Because PIS only re-tunes the weights LBS already consumes, it slots into any LBS-based pipeline---generative-3D outputs, automatic-rigging predictions, and the LBS pathways of game engines and AR/VR renderers---without touching shaders. On Objaverse-LVIS Balanced-102 (98 valid models, 51 LVIS categories), PIS reduces fragility (per-vertex deformation variance under random poses), ARAP energy, Jacobian anisotropy, and area distortion by $35.5$\%, $39.3$\%, $19.8$\%, and $43.2$\% on average; $93.9$\% of models improve on all four simultaneously, Wilcoxon $p<10^{-10}$. On the worst-case slice---the top decile of UniRig-initialized fragility, where animation visibly tears---PIS reduces unstable vertices by up to $10.2\times$. Effective bone count and weight entropy are unchanged, and the optimization runs in $\sim$30~s/model on a single H200 GPU.