LORAMI: Low‐Rank Adaptation for Multi‐Identity Physics‐Based Face Rigs

Abstract Physics‐based simulation can augment facial rigs with high‐quality deformations, but at steep computational costs. While neural surrogates can substantially reduce computation time, existing methods do not generalize across identity variations and instead require expensive per‐case retraining. In this work, we present LORAMI—a method for learning physics‐based face rigs over a continuous space of identities using low‐rank adaptation. LORAMI addresses the challenge of identity variation through a novel architecture that combines a shared neural surrogate with low‐rank weight adaptations. Instead of training a dense model across identity space, we modulate a shared base network using low‐rank factors. These factors are scaled by diagonal matrices predicted from identity parameters. This design enables efficient modeling of identity‐dependent variations while preserving the generic deformation behavior of the underlying physics‐based rig. Our experiments show that LORAMI achieves deformation accuracy on par with single‐identity models and outperforms fully dense identity‐conditioned networks. As a result, our method enables real‐time physics‐based facial animation with continuous identity control.