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Research map / 3D vision / Face and shape

Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild

CVPR 2020 · 3D vision › Face and shape · approach: Articulated and non-rigid shape learning · 246 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add albedo flipping to enforce canonical symmetry (ablation)Ours on BFM · BFMSIDE (×10^-2): 2.916 -> 0.793Helped
Add depth flipping to enforce canonical symmetry (ablation)Ours on BFM · BFMSIDE (×10^-2): 1.139 -> 0.793Helped
Compute shading from predicted depth and light instead of predicting a shading map (ablation)Ours on BFM · BFMSIDE (×10^-2): 2.406 -> 0.793Helped
Add perceptual loss (ablation)Ours on BFM · BFMSIDE (×10^-2): 0.931 -> 0.793Helped
Replace the ImageNet-pretrained perceptual encoder with a self-supervised encoder (ablation)Ours on BFM · BFMSIDE (×10^-2): 0.793 -> 0.815No clear effect
Add learned confidence maps (ablation)Ours on BFM · BFMSIDE (×10^-2): 0.829 -> 0.793No clear effect
Add learned confidence maps to handle asymmetric image perturbations (ablation)Ours on perturbed BFM · BFM with asymmetric perturbationsSIDE (×10^-2): 2.141 -> 0.878Helped
Use CelebA pre-trainingOurs on 3DFAW · 3DFAW frontal facesDepth Corr.: 48.98 -> 54.65Helped

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