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Rotate-and-Render: Unsupervised Photorealistic Face Rotation From Single-View Images

CVPR 2020 · 3D vision › Face and shape · approach: Face reflectance and relightable rendering · 141 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Augment Res18 training data with rotated facesRes18 trained on CASIA · LFWaccuracy (%): 98.77 -> 98.95Helped
Augment Res18 training data with rotated facesRes18 trained on MS1MV2 · LFWaccuracy (%): 99.63 -> 99.68Helped
Augment Res18 training data with rotated facesRes18 trained on CASIA · IJB-Averification rate @FAR=.01: 90.57±1.2 -> 91.98±0.7Helped
Augment Res18 training data with rotated facesRes18 trained on MS1MV2 · IJB-Averification rate @FAR=.01: 97.28±0.6 -> 97.30±0.6No clear effect
Augment R18 training data with rotated facesR18 trained on MS1MV2 · MegaFace 1-millionrank-1 identification accuracy (%): 97.00 -> 97.48Helped
Augment R50 training data with rotated facesR50 trained on MS1MV2 · MegaFace 1-millionrank-1 identification accuracy (%): 98.26 -> 98.44Helped
Add VGG loss (ablation)Rotate-and-Render · LFWFID: 93.1 -> 83.1Helped
Add VGG loss (ablation)Rotate-and-Render · IJB-AFID: 126.5 -> 70.9Helped
Use multi-scale discriminator and feature matching loss instead of Pix2Pix PatchGAN with image-level L1 loss (ablation)Rotate-and-Render · LFWFID: 83.9 -> 83.1Helped
Use multi-scale discriminator and feature matching loss instead of Pix2Pix PatchGAN with image-level L1 loss (ablation)Rotate-and-Render · IJB-AFID: 132.3 -> 70.9Helped

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