Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915194818527232 |
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| author | Büchner, Tim Anders, Christoph Guntinas-Lichius, Orlando Denzler, Joachim |
| author_facet | Büchner, Tim Anders, Christoph Guntinas-Lichius, Orlando Denzler, Joachim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis Büchner, Tim Anders, Christoph Guntinas-Lichius, Orlando Denzler, Joachim Computer Vision and Pattern Recognition 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. |
| title | Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.09556 |