MagPlus: Bridging Micro-to-Regular Facial Expressions through Learnable Magnification

Sliman Jammal; Andrei Sharf

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arXiv · 2606.13312

MagPlus: Bridging Micro-to-Regular Facial Expressions through Learnable Magnification

Sliman Jammal·Andrei Sharf

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Abstract

Facial micro-expressions are subtle and short-lived facial movements that provide important cues about genuine human emotions. However, modeling and generating them remains difficult because annotated micro-expression data is limited and the underlying facial motions are extremely weak. Existing micro-expression generation methods therefore often suffer from limited quality, weak robustness, and poor generalization. We propose MagPlus, a transferable micro-expression processing pipeline that connects micro-expression analysis with standard facial animation models. Instead of training a dedicated generator from scratch, MagPlus learns to magnify subtle facial motions into the range of regular facial expressions, transforming micro-expressions into signals that are compatible with existing facial expression processing models. The magnified sequence is then used by a standard facial expression model for tasks such as transfer and synthesis. A complementary DeMagPlus module then restores the generated motion back to realistic micro-expression intensity levels while preserving the synthesized dynamics. We evaluate the framework using four facial animation models: FOMM, FSRT, MetaPortrait, and EmoPortraits. None of these models are trained on micro-expression data. Experiments show that MagPlus-DeMagPlus enables pretrained macro-expression models to generate more realistic micro-expression motion without retraining the backbones.

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BibTeX RIS

Sliman Jammal, Andrei Sharf. 2026-06-11. MagPlus: Bridging Micro-to-Regular Facial Expressions through Learnable Magnification. https://arxiv.org/abs/2606.13312

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