SemanticFace: Semantic Facial Action Estimation via Semantic Distillation in Interpretable Space

Zejian Kang; Kai Zheng; Yuanchen Fei; Wentao Yang; Hongyuan Zou; Xiangru Huang

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arXiv · 2603.14827

SemanticFace: Semantic Facial Action Estimation via Semantic Distillation in Interpretable Space

Zejian Kang·Kai Zheng·Yuanchen Fei·Wentao Yang·Hongyuan Zou·Xiangru Huang

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Abstract

Facial action estimation from a single image is often formulated as predicting or fitting parameters in compact expression spaces, which lack explicit semantic interpretability. However, many practical applications, such as avatar control and human-computer interaction, require interpretable facial actions that correspond to meaningful muscle movements. In this work, we propose SemanticFace, a framework for facial action estimation in the interpretable ARKit blendshape space that reformulates coefficient prediction as structured semantic reasoning. SemanticFace adopts a two-stage semantic distillation paradigm: it first derives structured semantic supervision from ground-truth ARKit coefficients and then distills this knowledge into a multimodal large language model to predict interpretable facial action coefficients from images. Extensive experiments demonstrate that language-aligned semantic supervision improves both coefficient accuracy and perceptual consistency, while enabling strong cross-identity generalization and robustness to large domain shifts, including cartoon faces.

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Zejian Kang, Kai Zheng, Yuanchen Fei, Wentao Yang, Hongyuan Zou, Xiangru Huang. 2026-03-18. SemanticFace: Semantic Facial Action Estimation via Semantic Distillation in Interpretable Space. https://arxiv.org/abs/2603.14827

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