CVMar 12, 2025

Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis

arXiv:2503.09556v13 citationsh-index: 5CVPR
Originality Highly original
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This addresses a critical limitation in fields like psychology and medicine where existing methods fail due to occlusion, offering a new paradigm for multi-modal facial analysis.

The paper tackles the problem of reconstructing facial expressions from electromyography (sEMG) data despite electrode occlusion, achieving faithful geometry and appearance reconstruction through a novel method that decouples facial geometry and appearance using a 3D Morphable Model and neural translation.

The relationship between muscle activity and resulting facial expressions is crucial for various fields, including psychology, medicine, and entertainment. The synchronous recording of facial mimicry and muscular activity via surface electromyography (sEMG) provides a unique window into these complex dynamics. Unfortunately, existing methods for facial analysis cannot handle electrode occlusion, rendering them ineffective. Even with occlusion-free reference images of the same person, variations in expression intensity and execution are unmatchable. Our electromyography-informed facial expression reconstruction (EIFER) approach is a novel method to restore faces under sEMG occlusion faithfully in an adversarial manner. We decouple facial geometry and visual appearance (e.g., skin texture, lighting, electrodes) by combining a 3D Morphable Model (3DMM) with neural unpaired image-to-image translation via reference recordings. Then, EIFER learns a bidirectional mapping between 3DMM expression parameters and muscle activity, establishing correspondence between the two domains. We validate the effectiveness of our approach through experiments on a dataset of synchronized sEMG recordings and facial mimicry, demonstrating faithful geometry and appearance reconstruction. Further, we synthesize expressions based on muscle activity and how observed expressions can predict dynamic muscle activity. Consequently, EIFER introduces a new paradigm for facial electromyography, which could be extended to other forms of multi-modal face recordings.

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