We present a novel human body model formulated by an extensive set of
anthropocentric measurements, which is capable of generating a wide range of
human body shapes and poses. The proposed model enables direct modeling of
specific human identities through a deep generative architecture, which can
produce humans in any arbitrary pose. It is the first of its kind to have been
trained end-to-end using only synthetically generated data, which not only
provides highly accurate human mesh representations but also allows for precise
anthropometry of the body. Moreover, using a highly diverse animation library,
we articulated our synthetic humans' body and hands to maximize the diversity
of the learnable priors for model training. Our model was trained on a dataset
of $100k$ procedurally-generated posed human meshes and their corresponding
anthropometric measurements. Our synthetic data generator can be used to
generate millions of unique human identities and poses for non-commercial
academic research purposes.

我们提出了一种新颖的人体模型，通过一套广泛的人体测量数据生成，可产生各种人体形状和姿势。该模型通过深度生成架构，直接建模具体的人的身份，能够在任意姿势下生成人体。它是首个只使用合成数据进行端到端训练的模型，不仅提供高度准确的人体网格表示，还允许对人体的精确测量。此外，我们利用多样的动画库对合成人体的身体和手部进行了良好的描述，以最大化模型训练的可学习先验的多样性。我们的模型使用了 10 万个程序生成的姿势人体网格及其对应的人体测量数据进行训练。我们的合成数据生成器可用于为非商业学术研究目的生成数百万个独特的人体身份和姿势。

AnthroNet：基于人体测量的条件生成人类

AnthroNet: Conditional Generation of Humans via Anthropometrics

Due to its powerful capability of representation learning and high-efficiency
computation, deep hashing has made significant progress in large-scale image
retrieval. However, deep hashing networks are vulnerable to adversarial
examples, which is a practical secure problem but seldom studied in
hashing-based retrieval field. In this paper, we propose a novel
prototype-supervised adversarial network (ProS-GAN), which formulates a
flexible generative architecture for efficient and effective targeted hashing
attack. To the best of our knowledge, this is the first generation-based method
to attack deep hashing networks. Generally, our proposed framework consists of
three parts, i.e., a PrototypeNet, a generator, and a discriminator.
Specifically, the designed PrototypeNet embeds the target label into the
semantic representation and learns the prototype code as the category-level
representative of the target label. Moreover, the semantic representation and
the original image are jointly fed into the generator for a flexible targeted
attack. Particularly, the prototype code is adopted to supervise the generator
to construct the targeted adversarial example by minimizing the Hamming
distance between the hash code of the adversarial example and the prototype
code. Furthermore, the generator is against the discriminator to simultaneously
encourage the adversarial examples visually realistic and the semantic
representation informative. Extensive experiments verify that the proposed
framework can efficiently produce adversarial examples with better targeted
attack performance and transferability over state-of-the-art targeted attack
methods of deep hashing. The related codes could be available at
this https URL .

本文中提出了一个使用生成式方法构建的新型原型监督式对抗网络（ProS-GAN），用于攻击深度哈希网络，得到更好的攻击性能和可转移性。

原型监督的对抗网络用于深度哈希的有针对性攻击

Prototype-supervised Adversarial Network for Targeted Attack of Deep  Hashing

We propose the first stochastic framework to employ uncertainty for RGB-D
saliency detection by learning from the data labeling process. Existing RGB-D
saliency detection models treat this task as a point estimation problem by
predicting a single saliency map following a deterministic learning pipeline.
We argue that, however, the deterministic solution is relatively ill-posed.
Inspired by the saliency data labeling process, we propose a generative
architecture to achieve probabilistic RGB-D saliency detection which utilizes a
latent variable to model the labeling variations. Our framework includes two
main models: 1) a generator model, which maps the input image and latent
variable to stochastic saliency prediction, and 2) an inference model, which
gradually updates the latent variable by sampling it from the true or
approximate posterior distribution. The generator model is an encoder-decoder
saliency network. To infer the latent variable, we introduce two different
solutions: i) a Conditional Variational Auto-encoder with an extra encoder to
approximate the posterior distribution of the latent variable; and ii) an
Alternating Back-Propagation technique, which directly samples the latent
variable from the true posterior distribution. Qualitative and quantitative
results on six challenging RGB-D benchmark datasets show our approach's
superior performance in learning the distribution of saliency maps. The source
code is publicly available via our project page:
this https URL

本文提出了一种基于不确定性的随机框架，通过学习数据标记过程实现 RGB-D 显著性检测，通过引入一个潜在变量来建模标记变化，包括一个生成器模型和一个推理模型，并采用条件变分自动编码器和交替反向传播技术来推断潜在变量。在六个挑战性的 RGB-D 基准数据集上，实验表明我们的方法在学习显著性图分布方面具有优异性能。