PriSM: Parsing and Style-Mixed Consistency for Unsupervised Domain Adaptation in Facial Landmark Detection

摘要

Facial landmark detectors trained on real human faces oftenfail to generalize effectively to stylized domains such as caricatures andartistic portraits, necessitating unsupervised domain adaptation (UDA).Although self-training is a widely used UDA strategy to bridge do-main gaps, it frequently breaks down under large domain shifts as it isprone to amplifying confident yet erroneous pseudo-label predictions. Tothis end, we propose PriSM (Parsing and Style-Mixed Consistency), anovel method for robust landmark pseudo-label validation. PriSM lever-ages two complementary signals: a Parsing Network enforces high-levelstructural alignment through face parsing consistency, while a StyleMixNetwork enforces fine-grained geometric constraints by reducing the re-construction error between the input face and another face synthesizedfrom the pseudo-label’s structure and the input face’s appearance. Ex-tensive experiments on the challenging CariFace and ArtiFace bench-marks under the UDA setting demonstrate that PriSM significantly out-performs existing state-of-the-art methods and exhibits strong gener-alizability to unseen domains. The source code is available at: https://github.com/jerrychild999922/PriSM-UDA.