We formulate SVBRDF estimation from photographs as a diffusion task. To model
the distribution of spatially varying materials, we first train a novel
unconditional SVBRDF diffusion backbone model on a large set of 312,165
synthetic spatially varying material exemplars. This SVBRDF diffusion backbone
model, named MatFusion, can then serve as a basis for refining a conditional
diffusion model to estimate the material properties from a photograph under
controlled or uncontrolled lighting. Our backbone MatFusion model is trained
using only a loss on the reflectance properties, and therefore refinement can
be paired with more expensive rendering methods without the need for
backpropagation during training. Because the conditional SVBRDF diffusion
models are generative, we can synthesize multiple SVBRDF estimates from the
same input photograph from which the user can select the one that best matches
the users' expectation. We demonstrate the flexibility of our method by
refining different SVBRDF diffusion models conditioned on different types of
incident lighting, and show that for a single photograph under colocated flash
lighting our method achieves equal or better accuracy than existing SVBRDF
estimation methods.

我们将从照片中估计 SVBRDF 作为一项扩散任务，并使用一种新颖的无条件 SVBRDF 扩散基础模型 MatFusion 对分布具有空间变化的材料进行建模，然后通过条件扩散模型细化该基础模型以估计照片中的材料特性，并展示了我们的方法在不同类型的入射光照条件下对单张照片的精度能够达到或超过现有的 SVBRDF 估计方法。

MatFusion：一种用于 SVBRDF 捕获的生成扩散模型

MatFusion: A Generative Diffusion Model for SVBRDF Capture

Recent research used machine learning methods to predict a person's sexual
orientation from their photograph (Wang and Kosinski, 2017). To verify this
result, two of these models are replicated, one based on a deep neural network
(DNN) and one on facial morphology (FM). Using a new dataset of 20,910
photographs from dating websites, the ability to predict sexual orientation is
confirmed (DNN accuracy male 68%, female 77%, FM male 62%, female 72%). To
investigate whether facial features such as brightness or predominant colours
are predictive of sexual orientation, a new model based on highly blurred
facial images was created. This model was also able to predict sexual
orientation (male 63%, female 72%). The tested models are invariant to
intentional changes to a subject's makeup, eyewear, facial hair and head pose
(angle that the photograph is taken at). It is shown that the head pose is not
correlated with sexual orientation. While demonstrating that dating profile
images carry rich information about sexual orientation these results leave open
the question of how much is determined by facial morphology and how much by
differences in grooming, presentation and lifestyle. The advent of new
technology that is able to detect sexual orientation in this way may have
serious implications for the privacy and safety of gay men and women.

使用机器学习方法从照片中预测一个人的性取向，得到了一些验证性的结果。这些方法对于化妆、眼镜、面部毛发和头部姿势等特征不敏感，但它们对保护同性恋者的隐私和安全等方面可能带来严重的影响。

一项复制性研究：机器学习模型能够从面部图像预测性取向

A Replication Study: Machine Learning Models Are Capable of Predicting  Sexual Orientation From Facial Images

Current state of the art object recognition architectures achieve impressive
performance but are typically specialized for a single depictive style (e.g.
photos only, sketches only). In this paper, we present SwiDeN : our
Convolutional Neural Network (CNN) architecture which recognizes objects
regardless of how they are visually depicted (line drawing, realistic shaded
drawing, photograph etc.). In SwiDeN, we utilize a novel `deep' depictive
style-based switching mechanism which appropriately addresses the
depiction-specific and depiction-invariant aspects of the problem. We compare
SwiDeN with alternative architectures and prior work on a 50-category Photo-Art
dataset containing objects depicted in multiple styles. Experimental results
show that SwiDeN outperforms other approaches for the depiction-invariant
object recognition problem.

本文介绍了 SwiDeN—— 一种基于卷积神经网络（CNN）的物体识别架构，它可以识别出以不同方式视觉呈现的对象。这种架构采用了一种新颖的 “深度” 视觉呈现样式切换机制，可以适当地解决问题的特异性和不变性。实验表明，SwiDeN 在 50 个类别的照片 - 艺术数据集上的表现优于其他方法。