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Research map / 3D vision / Face and shape

DeepFaceFlow: In-the-Wild Dense 3D Facial Motion Estimation

CVPR 2020 · 3D vision › Face and shape · approach: 3D morphable face model regression · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DeepFaceFlow instead of LiteNet for 3D facial flow3D facial flow estimation · 4DFAB testAEPE: 1.5 -> 1.3Helped
Use DeepFaceFlow instead of LiteNet for 3D facial flow3D facial flow estimation · Face3DVid testAEPE: 2.2 -> 1.77Helped
Include all PNCC coordinate channels rather than only the z channel (ablation)DeepFaceFlow · 4DFAB testAEPE: 1.6 -> 1.3Helped
Include all PNCC coordinate channels rather than only the z channel (ablation)DeepFaceFlow · Face3DVid testAEPE: 1.971 -> 1.77Helped
Use DeepFaceFlow instead of the strongest scratch-trained baseline, LiteNet2D facial flow estimation · 4DFAB testAEPE: 1.018 -> 0.941Helped
Use DeepFaceFlow instead of the strongest scratch-trained baseline, LiteNet2D facial flow estimation · Face3DVid testAEPE: 1.268 -> 1.096Helped
Include all PNCC coordinate channels rather than only the z channel (ablation)DeepFaceFlow · 4DFAB testAEPE: 0.99 -> 0.941Helped
Include all PNCC coordinate channels rather than only the z channel (ablation)DeepFaceFlow · Face3DVid testAEPE: 1.176 -> 1.096Helped
Replace FlowNet2 with DeepFaceFlow 3D flowVideo-to-video self-reenactment · Self-reenactment videos 1–4average RGB distance: 7.5 -> 6.3; 9.5 -> 7.9; 8.7 -> 7.5; 9.2 -> 7.7Helped

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