face features as feature images ψrnd ∈ R112×112×16 given
viewpoints G∗ = (R∗, T∗) randomly generated on a unit
sphere facing the origin. We apply occlusion augmenta-
tions [44] that randomly mask out a rectangular region in
the rendered feature image and replace with mean values
of the corresponding feature channels. The random occlu-
sion augmentation forces the network to be robust to out-
lier inputs, and empirically helps network to make robust
predictions in presence of occlusions and in case of out-of-
distribution appearance.
Loss and inference. We use the geodesic distance between
the ground-truth and predicted rotations as a loss to update
PoseNet,

Lgeo = || log(R∗RT )||, R = PoseNet(ψrnd),

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BANMo- Building Animatable 3D Neural Models from Many Casual Videos