Monocular 3D face reconstruction is a wide-spread topic, and existing
approaches tackle the problem either through fast neural network inference or
offline iterative reconstruction of face geometry. In either case
carefully-designed energy functions are minimized, commonly including loss
terms like a photometric loss, a landmark reprojection loss, and others. In
this work we propose a new loss function for monocular face capture, inspired
by how humans would perceive the quality of a 3D face reconstruction given a
particular image. It is widely known that shading provides a strong indicator
for 3D shape in the human visual system. As such, our new 'perceptual' shape
loss aims to judge the quality of a 3D face estimate using only shading cues.
Our loss is implemented as a discriminator-style neural network that takes an
input face image and a shaded render of the geometry estimate, and then
predicts a score that perceptually evaluates how well the shaded render matches
the given image. This 'critic' network operates on the RGB image and geometry
render alone, without requiring an estimate of the albedo or illumination in
the scene. Furthermore, our loss operates entirely in image space and is thus
agnostic to mesh topology. We show how our new perceptual shape loss can be
combined with traditional energy terms for monocular 3D face optimization and
deep neural network regression, improving upon current state-of-the-art
results.

通过使用阴影线索，我们提出了一种新的适用于单目人脸捕捉的感知形状损失函数，该函数能够评估 3D 人脸估计的质量，并且能够优化单目 3D 人脸重建和深度神经网络回归，提高当前技术水平的结果。

单目三维人脸重建的感知形状损失

A Perceptual Shape Loss for Monocular 3D Face Reconstruction

Early work in computer vision considered a host of geometric cues for both
shape reconstruction and recognition. However, since then, the vision community
has focused heavily on shading cues for reconstruction, and moved towards
data-driven approaches for recognition. In this paper, we reconsider these
perhaps overlooked "boundary" cues (such as self occlusions and folds in a
surface), as well as many other established constraints for shape
reconstruction. In a variety of user studies and quantitative tasks, we
evaluate how well these cues inform shape reconstruction (relative to each
other) in terms of both shape quality and shape recognition. Our findings
suggest many new directions for future research in shape reconstruction, such
as automatic boundary cue detection and relaxing assumptions in shape from
shading (e.g. orthographic projection, Lambertian surfaces).

该论文重新考虑了在形状重建中可能被忽视的 “边界” 线索以及其他已建立的约束条件，并通过多项用户研究和定量任务评估了这些线索相对于彼此的信息量，结果表明这些线索在未来的形状重建研究中提供了很多新的方向。