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Research map / 3D vision / Face and shape

Multi-View Consistency as Supervisory Signal for Learning Shape and Pose Prediction

CVPR 2018 · 3D vision › Face and shape · approach: Articulated and non-rigid shape learning · 217 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
learn shape and pose jointly using multi-view consistency without pose supervisionsingle-view shape and pose prediction · ShapeNet meanmean IoU: 0.62 vs 0.55Helped
use depth rather than mask verification (ablation)multi-view training without rotation supervision · ShapeNet meanmean IoU: 0.55 vs 0.53No clear effect
remove known camera translation (ablation)multi-view training without rotation supervision (Mask) · ShapeNet meanmean IoU: 0.55 -> 0.40Hurt
remove known camera translation (ablation)multi-view training without rotation supervision (Depth) · ShapeNet meanmean IoU: 0.53 -> 0.47Hurt
remove known camera translation (ablation)multi-view pose prediction with mask verification · ShapeNet meanpose Accπ/6: 0.79 -> 0.54Hurt
remove known camera translation (ablation)multi-view pose prediction with depth verification · ShapeNet meanpose Accπ/6: 0.76 -> 0.61Hurt
add an approximate pose prior and remove it midway through training (ablation)pose prediction network · ShapeNettraining successHelped
remove the pose prior (ablation)pose prediction network · ShapeNetpose diversityHurt
use depth instead of mask verification (ablation)shape prediction for chairs · ShapeNet chairsshape concavity recoveryHelped

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