Recent work has shown the benefits of synthetic data for use in computer vision, with applications ranging from autonomous driving to face landmark detection and reconstruction. There are a number of benefits of using synthetic data from privacy preservation and bias elimination to quality and feasibility of annotation. Generating human-centered synthetic data is a particular challenge in terms of realism and domain-gap, though recent work has shown that effective machine learning models can be trained using synthetic face data alone. We show that this can be extended to include the full body by building on the pipeline of Wood et al. to generate synthetic images of humans in their entirety, with ground-truth annotations for computer vision applications. In this report we describe how we construct a parametric model of the face and body, including articulated hands; our rendering pipeline to generate realistic images of humans based on this body model; an approach for training DNNs to regress a dense set of landmarks covering the entire body; and a method for fitting our body model to dense landmarks predicted from multiple views.

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor $\gamma$ goes to $1$, and moreover, when $\gamma$ is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

我们提出了一种自我监督的方法，用于预测需要良好牵引力才能导航的轮式移动机器人的可穿越路径。我们的算法称为Wayfast（无路线自动驾驶系统用于遍历性），使用RGB和深度数据以及导航经验，自主在室外非结构化环境中自主生成可遍历的路径。我们的主要灵感是，可以使用动力动力学模型估算滚动机器人的牵引力。使用在线退化的视野估计器提供的牵引力估计值，我们能够以自我监督的方式训练遍历性预测神经网络，而无需以前的方法使用的启发式方法。我们通过在各种环境中进行广泛的现场测试来证明Wayfast的有效性，从沙滩到森林檐篷和积雪覆盖的草田不等。我们的结果清楚地表明，Wayfast可以学会避免几何障碍物以及不可传输的地形，例如雪，这很难避免使用仅提供几何数据（例如LiDAR）的传感器。此外，我们表明，基于在线牵引力估计的培训管道比其他基于启发式的方法更有效率。

增强现实应用程序开始改变体育广播的方式，为粉丝提供更丰富的体验和宝贵的见解。增强现实系统的第一步是摄像机校准，可能基于检测竞争环的线条标记。大多数现有的线路检测建议都取决于边缘检测和霍夫变换，但是径向失真和外部边缘会导致线标记的不准确或虚假检测。我们提出了一种新型策略，以自动准确细分并分类线标记。首先，由于随机流域变换对径向扭曲是可靠的，因此将线点分割了，因为它没有对线直度的假设，并且不受球员或球的存在影响。然后，由于非常有效的过程，该线点与原始结构（直线和椭圆形）链接在一起，该过程对每个图像中出现的原始数量的数量没有任何假设。该策略已在一个新的和公共数据库中测试，该数据库由五个体育场的比赛中的60个注释图像组成。所获得的结果证明，所提出的策略比现有方法更强大，更准确，即使在具有挑战性的条件下也可以实现成功的线标记检测。

对深层分类器的输入以最大化其错误分类速率的添加剂不可察觉的扰动的设计是对抗机器学习的主要重点。另一种方法是使用类似GAN的结构从头开始合成对抗性示例，尽管使用了大量的训练数据。相比之下，本文考虑了对抗性例子的一声合成。从头开始合成输入，以在预训练模型的输出下诱导任意软预测，同时保持与指定输入的高相似性。为此，我们提出了一个问题，该问题在训练有素的模型的所需和输出分布与此类输入与合成示例之间的相似性之间的距离上编码目标。我们证明了配制的问题是NP完整的。然后，我们将一种生成方法推进了解决方案，在该解决方案中，通过优化双重目标的替代损失函数，将对抗性示例作为生成网络的输出进行了迭代更新。我们证明了框架和方法的普遍性和多功能性，该框架和方法是通过应用于目标对抗攻击的设计，决策边界样本的产生以及置信分类低的综合输入的。该方法进一步扩展到具有不同软输出规格的模型集合。实验结果验证了针对最先进的算法以标准剂进行的靶向和置信降低攻击方法。