Intrinsic images, in the original sense, are image-like maps of scene
properties like depth, normal, albedo or shading. This paper demonstrates that
StyleGAN can easily be induced to produce intrinsic images. The procedure is
straightforward. We show that, if StyleGAN produces $G({w})$ from latents
${w}$, then for each type of intrinsic image, there is a fixed offset ${d}_c$
so that $G({w}+{d}_c)$ is that type of intrinsic image for $G({w})$. Here
${d}_c$ is {\em independent of ${w}$}. The StyleGAN we used was pretrained by
others, so this property is not some accident of our training regime. We show
that there are image transformations StyleGAN will {\em not} produce in this
fashion, so StyleGAN is not a generic image regression engine.
It is conceptually exciting that an image generator should ``know'' and
represent intrinsic images. There may also be practical advantages to using a
generative model to produce intrinsic images. The intrinsic images obtained
from StyleGAN compare well both qualitatively and quantitatively with those
obtained by using SOTA image regression techniques; but StyleGAN's intrinsic
images are robust to relighting effects, unlike SOTA methods.

本篇论文探讨了 StyleGAN 生成的图像中内在属性图像的表征方式，并较之 SOTA 方法更为鲁棒。

StyleGAN 知道 Normal、Depth、Albedo 等等

StyleGAN knows Normal, Depth, Albedo, and More

A fundamental problem in computer vision is that of inferring the intrinsic,
3D structure of the world from flat, 2D images of that world. Traditional
methods for recovering scene properties such as shape, reflectance, or
illumination rely on multiple observations of the same scene to overconstrain
the problem. Recovering these same properties from a single image seems almost
impossible in comparison -- there are an infinite number of shapes, paint, and
lights that exactly reproduce a single image. However, certain explanations are
more likely than others: surfaces tend to be smooth, paint tends to be uniform,
and illumination tends to be natural. We therefore pose this problem as one of
statistical inference, and define an optimization problem that searches for the
*most likely* explanation of a single image. Our technique can be viewed as a
superset of several classic computer vision problems (shape-from-shading,
intrinsic images, color constancy, illumination estimation, etc) and
outperforms all previous solutions to those constituent problems.

本文研究计算机视觉中的一个基本问题，即如何从平面图像中推断出世界的内在 3D 结构，并提出一种统计推断的优化问题，通过考虑表面光滑、颜色均匀、光照自然等先验知识，提出一种能够从单张图像中还原场景属性，包括形状、反射率和光照等的技术。该方法包含多个计算机视觉问题的超集，并优于以前所有单独问题的解决方案。

从阴影中推断形状、照明和反射

Shape, Illumination, and Reflectance from Shading

Separating an image into reflectance and shading layers poses a challenge for
learning approaches because no large corpus of precise and realistic ground
truth decompositions exists. The Intrinsic Images in the Wild~(IIW) dataset
provides a sparse set of relative human reflectance judgments, which serves as
a standard benchmark for intrinsic images. A number of methods use IIW to learn
statistical dependencies between the images and their reflectance layer.
Although learning plays an important role for high performance, we show that a
standard signal processing technique achieves performance on par with current
state-of-the-art. We propose a loss function for CNN learning of dense
reflectance predictions. Our results show a simple pixel-wise decision, without
any context or prior knowledge, is sufficient to provide a strong baseline on
IIW. This sets a competitive baseline which only two other approaches surpass.
We then develop a joint bilateral filtering method that implements strong prior
knowledge about reflectance constancy. This filtering operation can be applied
to any intrinsic image algorithm and we improve several previous results
achieving a new state-of-the-art on IIW. Our findings suggest that the effect
of learning-based approaches may have been over-estimated so far. Explicit
prior knowledge is still at least as important to obtain high performance in
intrinsic image decompositions.

本文探讨使用先验知识在分离图像的内在成分中的反射率和阴影层时的高性能方法，并提出了一种基于 CNN 学习和使用先验知识的损失函数，后者可以实现强反射不变性的联合双边滤波方法，达到在我们提供的对多个算法进行内在成分分解的标准基准测试上的最佳性能。

反射自适应滤波提升本质图像估计

Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

We present a method for jointly predicting a depth map and intrinsic images
from single-image input. The two tasks are formulated in a synergistic manner
through a joint conditional random field (CRF) that is solved using a novel
convolutional neural network (CNN) architecture, called the joint convolutional
neural field (JCNF) model. Tailored to our joint estimation problem, JCNF
differs from previous CNNs in its sharing of convolutional activations and
layers between networks for each task, its inference in the gradient domain
where there exists greater correlation between depth and intrinsic images, and
the incorporation of a gradient scale network that learns the confidence of
estimated gradients in order to effectively balance them in the solution. This
approach is shown to surpass state-of-the-art methods both on single-image
depth estimation and on intrinsic image decomposition.

文章提出了一种利用联合 CRF 和卷积神经网络解决单个图像预测深度图和本征图的方法，该方法命名为联合卷积神经场模型 (JCNF)，该方法在单图深度估计和本征图分解两个任务上均超越了现有方法。