  Landmarks often play a key role in face analysis, but many aspects of
identity or expression cannot be represented by sparse landmarks alone. Thus,
in order to reconstruct faces more accurately, landmarks are often combined
with additional signals like depth images or techniques like differentiable
rendering. Can we keep things simple by just using more landmarks? In answer,
we present the first method that accurately predicts 10x as many landmarks as
usual, covering the whole head, including the eyes and teeth. This is
accomplished using synthetic training data, which guarantees perfect landmark
annotations. By fitting a morphable model to these dense landmarks, we achieve
state-of-the-art results for monocular 3D face reconstruction in the wild. We
show that dense landmarks are an ideal signal for integrating face shape
information across frames by demonstrating accurate and expressive facial
performance capture in both monocular and multi-view scenarios. This approach
is also highly efficient: we can predict dense landmarks and fit our 3D face
model at over 150FPS on a single CPU thread. Please see our website:
https://microsoft.github.io/DenseLandmarks/.
