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Research map / 3D vision / Face and shape

JR2Net: Joint Monocular 3D Face Reconstruction and Reenactment

AAAI 2023 · 3D vision › Face and shape · approach: Face reflectance and relightable rendering · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use dense pixel-level conditions and 3D reconstruction guidance for reenactmentJR2Net DCNet · VoxCeleb2 cross-identityEFL: 2.88 -> 2.50Helped
use dense pixel-level conditions and 3D reconstruction guidance for reenactmentJR2Net DCNet · VoxCeleb2 cross-identityFID: 41.48 -> 37.45Helped
train the expression module using reenactment-generated image-expression pairsRecAugNet · FaceWarehouseRMSE: 1.90 -> 1.78Helped
train RecNet semi-supervised with image-expression pairs and expression loss (ablation)RecNet · FaceWarehouseRMSE: 1.90 -> 1.98Hurt
replace the expression tracker with an empty ETNet (ablation)RecAugNet · FaceWarehouseRMSE: 1.89 -> 1.78Hurt
add generated image-expression pairs to RecNet training data (ablation)RecNet · FaceWarehouseRMSE: 1.90 -> 1.92Hurt
use rendered images instead of dense pixel-level conditions (ablation)DCNet · VoxCeleb2 same-identityL1: 0.042 -> 0.044Hurt
remove illumination control (ablation)DCNet · VoxCeleb2 same-identityL1: 0.042 -> 0.048Hurt
include warped source images as conditional input (ablation)DCNet · VoxCeleb2 same-identityL1: 0.045 -> 0.042Helped

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