The field of face analysis and personalization is moving towards developing more robust and flexible models that can handle cross-domain variations and preserve identity information. Recent studies have focused on improving the accuracy and generalizability of face recognition and retargeting methods across different domains and datasets. Notable advancements include the development of adversarial domain alignment techniques and disentangled representation learning methods. These innovations have led to significant improvements in cross-domain face recognition and retargeting tasks.

Noteworthy papers include: Graph-Attention Network with Adversarial Domain Alignment for Robust Cross-Domain Facial Expression Recognition, which achieved state-of-the-art results in cross-domain facial expression recognition. StyleYourSmile, a novel one-shot cross-domain face retargeting method that eliminates the need for curated multi-style paired data. Are Detectors Fair to Indian IP-AIGC, a study that highlights the need for representation-preserving adaptation and India-aware benchmark curation to close generalization gaps in AIGC detection. Training for Identity, Inference for Controllability, a unified approach to tuning-free face personalization that achieves high identity fidelity and flexible text controllability.