RetargetingFace: Bridging Heterogeneous Facial-Control Spaces with a Shared Expression Representation

Facial animation systems encode the same expressions in incompatible native control spaces. Pairwise vector regression can bridge these spaces, but requires a separate mapping for each source–target direction. We therefore study cross-system facial retargeting as native-control interoperability: preserving the expression being conveyed while retaining each system’s original control parameterization. Based on this perspective, we introduce RetargetingFace, a shared expression interface that connects different facial-control systems using condition-level correspondence rather than exact cross-rig coefficient pairs. RetargetingFace extracts neutral-relative visual evidence from expressive and neutral renderings, reducing dependence on source facial shape while preserving expression-induced changes. Expression supervision, conditional rig confusion, and cross-rig alignment organize these observations into a shared representation. Each rig-specific predictor trains on its own latent–control pairs and is reused across source rigs. We construct a controlled GNM-derived dataset with 532 expression states, strength and FLAME-shape variation, and condition-matched native FLAME/ARKit realizations. On held-out FLAME↔ARKit transfers, RetargetingFace improves mean human similarity by 18.7% over the best evaluated existing baseline; the mean gain on external captured expressions is 9.9%. With the representation frozen, independently trained GNM and MHR predictors extend the interface to new target and source rigs. On the reported FLAME/GNM/MHR transfers, human ratings and first-hop cosine favor our method over direction-specific Direct MLP.