Weakly Supervised Exaggeration Transfer for Caricature Generation With Cross-Modal Knowledge Distillation

Caricature generation aims to translate portrait photos into caricatures with exaggerated and hand-drawn artistic styles. Previous methods faced challenges in creating diverse and meaningful exaggeration effects, yielding unsatisfactory and uncontrollable results. To overcome this, we proposed ETCari, a novel weakly supervised exaggeration transfer network. ETCari enables the learning of diverse exaggeration caricature styles from various artists, better meeting individual customization requirements and achieving diversified exaggeration while retaining identity features. Specifically, we use the TPS control point deformation field as the ground truth, serving as the loss for weakly supervised learning to address the challenge of no labels. We convert input to an intermediate modality for domain adaptation, training a teacher model. Subsequently, we perform cross-modal knowledge distillation to train the student model, simplifying preprocessing during inference and avoiding the impact of face parser errors. Experiments on the WebCaricature dataset demonstrate that ETCari effectively performs exaggeration transfer, generating appealing caricatures.