MOCHI enables registration-free training of multi-view 3D face reconstruction by enforcing topological consistency via a pseudo-linear inverse kinematic solver, using synthetic-data-trained 2D landmarks for alignment, and new pointmap/normal losses plus test-time optimization to outperform prior art
arXiv:2207.11094 (2022)
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Fusing 3D facial motion descriptors with physiological signals via cross-modal attention improves stress detection AUROC from 52.7% to 92.0% on driving data.
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Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
MOCHI enables registration-free training of multi-view 3D face reconstruction by enforcing topological consistency via a pseudo-linear inverse kinematic solver, using synthetic-data-trained 2D landmarks for alignment, and new pointmap/normal losses plus test-time optimization to outperform prior art
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Combining Facial Videos and Biosignals for Stress Estimation During Driving
Fusing 3D facial motion descriptors with physiological signals via cross-modal attention improves stress detection AUROC from 52.7% to 92.0% on driving data.