近年来，人类面孔的影子化化身已经走了很长一段路，但是该地区的研究受到缺乏公开可用的高质量数据集的限制。在这项工作中，我们介绍了Multiface，这是一种新的多视图，高分辨率的人脸数据集，该数据集是从13个身份的神经面部渲染研究中收集的13个身份。我们介绍了Mugsy，这是一种大型多摄像机设备，可捕获面部表现的高分辨率同步视频。 Multiface的目的是缩小学术界高质量数据的可访问性的差距，并使VR触觉研究能够进行研究。随着数据集的释放，我们对不同模型体系结构对模型的新观点和表达式的插值能力进行消融研究。通过有条件的VAE模型作为我们的基线，我们发现添加空间偏见，纹理翘曲场和残差连接可改善新型视图合成的性能。我们的代码和数据可在以下网址获得：https：//github.com/facebookresearch/multiface

逼真的触觉需要高保真的身体建模和忠实的驾驶才能使动态合成的外观与现实无法区分。在这项工作中，我们提出了一个端到端框架，该框架解决了建模和推动真实人的全身化身方面的两个核心挑战。一个挑战是驾驶头像，同时忠实地遵守细节和动态，而这些细节和动态无法被全球低维参数化（例如身体姿势）所捕捉。我们的方法支持驾驶穿着皱纹和运动的衣服化身，而真正的驾驶表演者展出了训练语料库。与现有的全局状态表示或非参数屏幕空间方法不同，我们介绍了Texel对准功能 - 一种本地化表示，可以利用基于骨架的参数模型的结构先验和同时观察到的稀疏图像信号。另一个挑战是建模临时连贯的衣服头像，通常需要精确的表面跟踪。为了避免这种情况，我们通过将体积原语的混合物扩展到清晰的物体，提出了一种新型的体积化头像表示。通过明确合并表达，我们的方法自然而然地概括了看不见的姿势。我们还介绍了局部视点条件，从而导致了依赖视图的外观的概括。拟议的体积表示不需要高质量的网格跟踪作为先决条件，并且与基于网格的对应物相比，具有显着的质量改进。在我们的实验中，我们仔细研究了我们的设计选择，并证明了方法的功效，超过了最新方法在挑战驾驶方案方面的最新方法。

尽管最近在开发动画全身化身方面取得了进展，但服装的现实建模（人类自我表达的核心方面之一）仍然是一个开放的挑战。最先进的物理模拟方法可以以交互速度产生现实行为的服装几何形状。但是，建模光真逼真的外观通常需要基于物理的渲染，这对于交互式应用来说太昂贵了。另一方面，数据驱动的深度外观模型能够有效地产生逼真的外观，但在合成高度动态服装的几何形状和处理具有挑战性的身体套构型方面挣扎。为此，我们通过对服装的明确建模介绍了姿势驱动的化身，这些化身表现出逼真的服装动力学和从现实世界数据中学到的逼真的外观。关键的想法是引入一个在显式几何形状之上运行的神经服装外观模型：在火车时，我们使用高保真跟踪，而在动画时期，我们依靠物理模拟的几何形状。我们的关键贡献是一个具有物理启发的外观网络，能够生成具有视图依赖性和动态阴影效果的影像逼真的外观，即使对于看不见的身体透明构型也是如此。我们对我们的模型进行了彻底的评估，并在几种受试者和不同类型的衣服上展示了不同的动画结果。与以前关于影迷全身化身的工作不同，我们的方法甚至可以为宽松的衣服产生更丰富的动力和更现实的变形。我们还证明，我们的配方自然允许服装与不同人的头像一起使用，同时保持完全动画，因此首次可以采用新颖的衣服来实现逼真的化身。

虚拟网格是在线通信的未来。服装是一个人身份和自我表达的重要组成部分。然而，目前，在培训逼真的布置动画的远程介绍模型的必需分子和准确性中，目前无法使用注册衣服的地面真相数据。在这里，我们提出了一条端到端的管道，用于建造可驱动的服装代表。我们方法的核心是一种多视图图案的布跟踪算法，能够以高精度捕获变形。我们进一步依靠跟踪方法生产的高质量数据来构建服装头像：一件衣服的表达和完全驱动的几何模型。可以使用一组稀疏的视图来对所得模型进行动画，并产生高度逼真的重建，这些重建忠于驾驶信号。我们证明了管道对现实的虚拟电视应用程序的功效，在该应用程序中，从两种视图中重建了衣服，并且用户可以根据自己的意愿进行选择和交换服装设计。此外，当仅通过身体姿势驱动时，我们表现出一个具有挑战性的场景，我们可驾驶的服装Avatar能够生产出比最先进的面包质量明显更高的逼真的布几何形状。

where the highest resolution is required, using facial performance capture as a case in point.

Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.

Figure 1: Frankenstein (silver) and Adam (gold). This paper presents a 3D human model capable of concurrently tracking the large-scale posture of the body along with the smaller details of a persons facial expressions and hand gestures.

We present an approach to efficiently detect the 2D pose of multiple people in an image. The approach uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. The architecture encodes global context, allowing a greedy bottom-up parsing step that maintains high accuracy while achieving realtime performance, irrespective of the number of people in the image. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. Our method placed first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds the previous state-of-the-art result on the MPII Multi-Person benchmark, both in performance and efficiency.

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.