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Biomechanics-Guided Facial Action Unit Detection Through Force Modeling

CVPR 2023 · 3D vision › Face and shape · approach: 3D morphable face model regression · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
combine 3D force and position instead of using either alone (ablation)3D physics branch · BP4Daverage F1 (%): 46.4 and 53.9 -> 57.3Helped
combine the physics branch with the image branch (ablation)AU detection model · BP4Daverage F1 (%): 57.3 and 58.7 -> 64.1Helped
add 3D reconstruction loss (ablation)AU detection model · BP4D, 50% training dataaverage F1 (%): 39.0 -> 46.2Helped
add 3D reconstruction loss (ablation)AU detection model · BP4D, 20% training dataaverage F1 (%): 34.7 -> 44.3Helped
add 3D reconstruction loss (ablation)AU detection model · BP4D, 5% training dataaverage F1 (%): 27.9 -> 33.0Helped
use the biomechanics-based physics branch rather than the data-driven modelcross-dataset AU detection · BP4D to DISFA (overlapping AUs)average F1 (%): 32.1 -> 38.8Helped
use the proposed method instead of FAUAU detection · BP4Daverage F1 (%): 64.2 -> 64.1Hurt
use the proposed method instead of FAUAU detection · DISFAaverage F1 (%): 61.5 -> 58.2Hurt
use biomechanics-based force prediction instead of image-only prediction (ablation)AU1 detection · BP4DF1 (%): 46.3 -> 58.6Helped
use biomechanics-based force prediction instead of image-only prediction (ablation)AU15 detection · BP4DF1 (%): 48.1 -> 29.4Hurt
add force and position physics features to image features (ablation)AU detection model · BP4DF1 (%): AU1: 46.3 -> 57.4; AU15: 48.1 -> 47.3Mixed

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