我们提出了Blazepose Ghum整体，这是一种针对3D人体地标和姿势估计的轻型神经网络管道，专门针对实时的实时推论量身定制。Blazepose Ghum整体可以从单个RGB图像中捕获运动捕获，包括头像控制，健身跟踪和AR/VR效果。我们的主要贡献包括i）一种用于3D地面真相数据获取的新方法，ii）更新了3D身体跟踪，并使用其他手工标记和iii）从单眼图像中进行全身姿势估算。

人类性能捕获是一种非常重要的计算机视觉问题，在电影制作和虚拟/增强现实中具有许多应用。许多以前的性能捕获方法需要昂贵的多视图设置，或者没有恢复具有帧到帧对应关系的密集时空相干几何。我们提出了一种新颖的深度致密人体性能捕获的深层学习方法。我们的方法是基于多视图监督的弱监督方式培训，完全删除了使用3D地面真理注释的培训数据的需求。网络架构基于两个单独的网络，将任务解散为姿势估计和非刚性表面变形步骤。广泛的定性和定量评估表明，我们的方法在质量和稳健性方面优于现有技术。这项工作是DeepCAP的扩展版本，在那里我们提供更详细的解释，比较和结果以及应用程序。

了解来自第一人称观点的社交互动对于许多应用来说至关重要，从辅助机器人到AR / VR。谈论相互作用的第一步是理解人类的姿势和形状。但是，该领域的研究目前受到数据缺乏的阻碍。现有数据集根据大小，注释，地面真实捕获方式或相互作用的多样性有限。我们通过提出EGOBODY来解决这一缺点，这是一个用于复杂3D场景中的社交交互的新型大规模数据集。我们采用Microsoft Hololens2耳机来记录富裕的EGEntric数据流（包括RGB，深度，眼睛凝视，头部和手动跟踪）。为了获得准确的3D地面真理，我们将耳机用多kinect钻机校准并配合富有呈现的SMPL-X体网格到多视图RGB-D帧，重建3D人类姿势和相对于场景的形状。我们收集68个序列，跨越不同的社会学互动类别，并提出了从自我监视视图的3D全体姿态和形状估计的第一个基准。我们的数据集和代码将在https://sanweiliti.github.io/egobody/egobody.html中进行研究。

由于其许多潜在应用，从视频中估算人类运动是一个活跃的研究领域。大多数最先进的方法可以预测单个图像的人类形状和姿势估计，并且不利用视频中可用的时间信息。许多“野生”运动序列被移动的摄像机捕获，这为估计增加了混合的摄像头和人类运动的并发症。因此，我们介绍了Bodyslam，这是一种单眼大满贯系统，共同估计人体的位置，形状和姿势以及摄像机轨迹。我们还引入了一种新型的人类运动模型，以限制顺序身体姿势并观察场景的规模。通过通过移动的单眼相机捕获的人类运动的视频序列进行的一系列实验，我们证明了Bodyslam与单独估计这些估计相比，可以改善所有人体参数和相机的估计。

3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-thewild". However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets. Human annotators solely sort good and bad fits. This procedure leads to an initial dataset, UP-3D, with rich annotations. With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body. Using the 91 landmark pose estimator, we present state-ofthe art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure. We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale. The data, code and models are available for research purposes.* This work was performed while J. Romero and F. Bogo were with the MPI-IS 2 ; P. V. Gehler with the BCCN 1 and MPI-IS 2 .

以准确的，稳健和快速的方式拟合人体，手或面对稀疏输入信号的参数模型，这具有重要的是在AR和VR场景中显着改善浸入。解决这些问题的系统中的一个常见的第一步是直接从输入数据重新分配参数模型的参数。这种方法是快速，稳健的，并且是迭代最小化算法的良好起点。后者搜索最小的能量函数，通常由编码关于问题的结构的知识的数据项和前沿组成。虽然这无疑是一个非常成功的食谱，但前锋往往是手工定义的启发式，发现不同术语之间的正确平衡，以实现高质量的结果是一个非琐碎的任务。此外，转换和优化这些系统以表现方式运行，需要定制实现，要求从工程师和域专家进行大量时间投资。在这项工作中，我们建立了近期学习优化的进步，并提出了由Classic Levenberg-Marquardt算法启发的更新规则。我们展示了所提出的神经优化器对从2D地标的头戴式装置和面部配件的3D体表估计问题的有效性。我们的方法可以很容易地应用于新的模型拟合问题，并提供竞争替代方案，在准确性和速度方面都提供了良好的调谐“传统”模型拟合管道。

This paper addresses the problem of 3D human pose and shape estimation from a single image. Previous approaches consider a parametric model of the human body, SMPL, and attempt to regress the model parameters that give rise to a mesh consistent with image evidence. This parameter regression has been a very challenging task, with modelbased approaches underperforming compared to nonparametric solutions in terms of pose estimation. In our work, we propose to relax this heavy reliance on the model's parameter space. We still retain the topology of the SMPL template mesh, but instead of predicting model parameters, we directly regress the 3D location of the mesh vertices. This is a heavy task for a typical network, but our key insight is that the regression becomes significantly easier using a Graph-CNN. This architecture allows us to explicitly encode the template mesh structure within the network and leverage the spatial locality the mesh has to offer. Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location. Having recovered the complete 3D geometry of the mesh, if we still require a specific model parametrization, this can be reliably regressed from the vertices locations. We demonstrate the flexibility and the effectiveness of our proposed graphbased mesh regression by attaching different types of features on the mesh vertices. In all cases, we outperform the comparable baselines relying on model parameter regression, while we also achieve state-of-the-art results among model-based pose estimation approaches. 1

3D human whole-body pose estimation aims to localize precise 3D keypoints on the entire human body, including the face, hands, body, and feet. Due to the lack of a large-scale fully annotated 3D whole-body dataset, a common approach has been to train several deep networks separately on datasets dedicated to specific body parts, and combine them during inference. This approach suffers from complex training and inference pipelines because of the different biases in each dataset used. It also lacks a common benchmark which makes it difficult to compare different methods. To address these issues, we introduce Human3.6M 3D WholeBody (H3WB) which provides whole-body annotations for the Human3.6M dataset using the COCO Wholebody layout. H3WB is a large scale dataset with 133 whole-body keypoint annotations on 100K images, made possible by our new multi-view pipeline. Along with H3WB, we propose 3 tasks: i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, iii) 3D whole-body pose estimation from a single RGB image. We also report several baselines from popular methods for these tasks. The dataset is publicly available at \url{https://github.com/wholebody3d/wholebody3d}.

自上而下的方法主导了3D人类姿势和形状估计的领域，因为它们与人类的检测脱钩，并使研究人员能够专注于核心问题。但是，裁剪是他们的第一步，从一开始就丢弃了位置信息，这使自己无法准确预测原始摄像机坐标系中的全局旋转。为了解决此问题，我们建议将完整框架（悬崖）的位置信息携带到此任务中。具体而言，我们通过将裁剪图像功能与其边界盒信息连接在一起来养活更多的整体功能来悬崖。我们通过更广泛的全帧视图来计算2D再投影损失，进行了类似于图像中投射的人的投影过程。克里夫（Cliff）通过全球态度感知信息进行了喂养和监督，直接预测全球旋转以及更准确的明确姿势。此外，我们提出了一个基于Cliff的伪基真实注释，该注释为野外2D数据集提供了高质量的3D注释，并为基于回归的方法提供了至关重要的全面监督。对流行基准测试的广泛实验表明，悬崖的表现要超过先前的艺术，并在Agora排行榜上获得了第一名（SMPL-Algorithms曲目）。代码和数据可在https://github.com/huawei-noah/noah-research/tree/master/cliff中获得。

Figure 1: Given challenging in-the-wild videos, a recent state-of-the-art video-pose-estimation approach [31] (top), fails to produce accurate 3D body poses. To address this, we exploit a large-scale motion-capture dataset to train a motion discriminator using an adversarial approach. Our model (VIBE) (bottom) is able to produce realistic and accurate pose and shape, outperforming previous work on standard benchmarks.

用于3D人类传感的最新技术的进展目前受到3D地面真理的缺乏视觉数据集的限制，包括多个人，运动，在现实世界环境中运行，具有复杂的照明或遮挡，并且可能观察到移动相机。复杂的场景理解需要估计人类的姿势和形状以及手势，朝着最终将有用的度量和行为信号与自由视点相结合的表示来估计的表示。为了维持进步，我们建立了一个大型的照片 - 现实数据集，人类空间（HSPACE），用于复杂的合成室内和室外环境中的动画人。我们将百种不同的年龄，性别，比例和种族相结合，以及数百个动作和场景，以及身体形状的参数变化（总共1,600种不同的人类），以产生初始数据集超过100万帧。人类的动画是通过拟合表达的人体模型，以单身扫描人们来获得，其次是新的重新定位和定位程序，支持穿着人的人类的现实动画，身体比例的统计变化，以及联合一致的场景放置多个移动的人。资产在规模上自动生成，并与现有的实时渲染和游戏引擎兼容。具有评估服务器的数据集将可用于研究。我们的大规模分析了合成数据的影响，与实际数据和弱监管有关，强调了持续质量改进和限制了这种实际设置，与模型容量增加的实际设定的相当大的潜力。

大多数实时人类姿势估计方法都基于检测接头位置。使用检测到的关节位置，可以计算偏差和肢体的俯仰。然而，由于这种旋转轴仍然不观察，因此不能计算沿着肢体沿着肢体至关重要的曲折，这对于诸如体育分析和计算机动画至关重要。在本文中，我们引入了方向关键点，一种用于估计骨骼关节的全位置和旋转的新方法，仅使用单帧RGB图像。灵感来自Motion-Capture Systems如何使用一组点标记来估计全骨骼旋转，我们的方法使用虚拟标记来生成足够的信息，以便准确地推断使用简单的后处理。旋转预测改善了接头角度最佳报告的平均误差48％，并且在15个骨骼旋转中实现了93％的精度。该方法还通过MPJPE在原理数据集上测量，通过MPJPE测量，该方法还改善了当前的最新结果14％，并概括为野外数据集。

推断人类场景接触（HSC）是了解人类如何与周围环境相互作用的第一步。尽管检测2D人类对象的相互作用（HOI）和重建3D人姿势和形状（HPS）已经取得了重大进展，但单个图像的3D人习惯接触的推理仍然具有挑战性。现有的HSC检测方法仅考虑几种类型的预定义接触，通常将身体和场景降低到少数原语，甚至忽略了图像证据。为了预测单个图像的人类场景接触，我们从数据和算法的角度解决了上述局限性。我们捕获了一个名为“真实场景，互动，联系和人类”的新数据集。 Rich在4K分辨率上包含多视图室外/室内视频序列，使用无标记运动捕获，3D身体扫描和高分辨率3D场景扫描捕获的地面3D人体。 Rich的一个关键特征是它还包含身体上精确的顶点级接触标签。使用Rich，我们训练一个网络，该网络可预测单个RGB图像的密集车身场景接触。我们的主要见解是，接触中的区域总是被阻塞，因此网络需要能够探索整个图像以获取证据。我们使用变压器学习这种非本地关系，并提出新的身体场景接触变压器（BSTRO）。很少有方法探索3D接触；那些只专注于脚的人，将脚接触作为后处理步骤，或从身体姿势中推断出无需看现场的接触。据我们所知，BSTRO是直接从单个图像中直接估计3D身体场景接触的方法。我们证明，BSTRO的表现明显优于先前的艺术。代码和数据集可在https://rich.is.tue.mpg.de上获得。

本文介绍了一个新的大型多视图数据集，称为Humbi的人体表达式，具有天然衣物。 HUMBI的目标是为了便于建模特异性的外观和五个主要身体信号的几何形状，包括来自各种各样的人的凝视，面部，手，身体和服装。 107同步高清摄像机用于捕获772个跨性别，种族，年龄和风格的独特科目。使用多视图图像流，我们使用3D网格模型重建高保真体表达式，允许表示特定于视图的外观。我们证明HUMBI在学习和重建完整的人体模型方面非常有效，并且与人体表达的现有数据集互补，具有有限的观点和主题，如MPII-Gaze，Multi-Pie，Human 3.6m和Panoptic Studio数据集。基于HUMBI，我们制定了一种展开的姿态引导外观渲染任务的新基准挑战，其旨在大大延长了在3D中建模的不同人类表达式中的光敏性，这是真实的社会远程存在的关键能力。 Humbi公开提供http://humbi-data.net

Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync with the audio, natural facial expressions and head motions, and high frame quality. In this work, we present SPACE, which uses speech and a single image to generate high-resolution, and expressive videos with realistic head pose, without requiring a driving video. It uses a multi-stage approach, combining the controllability of facial landmarks with the high-quality synthesis power of a pretrained face generator. SPACE also allows for the control of emotions and their intensities. Our method outperforms prior methods in objective metrics for image quality and facial motions and is strongly preferred by users in pair-wise comparisons. The project website is available at https://deepimagination.cc/SPACE/

From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features. At test time, from video, the learned temporal representation give rise to smooth 3D mesh predictions. From a single image, our model can recover the current 3D mesh as well as its 3D past and future motion. Our approach is designed so it can learn from videos with 2D pose annotations in a semi-supervised manner. Though annotated data is always limited, there are millions of videos uploaded daily on the Internet. In this work, we harvest this Internet-scale source of unlabeled data by training our model on unlabeled video with pseudo-ground truth 2D pose obtained from an off-the-shelf 2D pose detector. Our experiments show that adding more videos with pseudo-ground truth 2D pose monotonically improves 3D prediction performance. We evaluate our model, Human Mesh and Motion Recovery (HMMR), on the recent challenging dataset of 3D Poses in the Wild and obtain state-of-the-art performance on the 3D prediction task without any fine-tuning. The project website with video, code, and data can be found at https://akanazawa.github.io/ human_dynamics/.

在本文中，我们考虑了同时找到和从单个2D图像中恢复多手的具有挑战性的任务。先前的研究要么关注单手重建，要么以多阶段的方式解决此问题。此外，常规的两阶段管道首先检测到手部区域，然后估计每个裁剪贴片的3D手姿势。为了减少预处理和特征提取中的计算冗余，我们提出了一条简洁但有效的单阶段管道。具体而言，我们为多手重建设计了多头自动编码器结构，每个HEAD网络分别共享相同的功能图并分别输出手动中心，姿势和纹理。此外，我们采用了一个弱监督的计划来减轻昂贵的3D现实世界数据注释的负担。为此，我们提出了一系列通过舞台训练方案优化的损失，其中根据公开可用的单手数据集生成具有2D注释的多手数据集。为了进一步提高弱监督模型的准确性，我们在单手和多个手设置中采用了几个功能一致性约束。具体而言，从本地功能估算的每只手的关键点应与全局功能预测的重新投影点一致。在包括Freihand，HO3D，Interhand 2.6M和RHD在内的公共基准测试的广泛实验表明，我们的方法在弱监督和完全监督的举止中优于基于最先进的模型方法。代码和模型可在{\ url {https://github.com/zijinxuxu/smhr}}上获得。

培训视频中人类姿势估计的最先进模型需要具有很难获得的注释的数据集。尽管最近已将变压器用于身体姿势序列建模，但相关方法依靠伪地真相来增强目前有限的培训数据可用于学习此类模型。在本文中，我们介绍了Posebert，Posebert是一个通过掩盖建模对3D运动捕获（MOCAP）数据进行全面训练的变压器模块。它是简单，通用和通用的，因为它可以插入任何基于图像的模型的顶部，以在基于视频的模型中使用时间信息。我们展示了Posebert的变体，不同的输入从3D骨骼关键点到全身或仅仅是手（Mano）的3D参数模型的旋转。由于Posebert培训是任务不可知论的，因此该模型可以应用于姿势细化，未来的姿势预测或运动完成等几个任务。我们的实验结果验证了在各种最新姿势估计方法之上添加Posebert始终提高其性能，而其低计算成本使我们能够在实时演示中使用它，以通过A的机器人手使机器人手通过摄像头。可以在https://github.com/naver/posebert上获得测试代码和型号。

Input Reconstruction Side and top down view Part Segmentation Input Reconstruction Side and top down view Part Segmentation Figure 1: Human Mesh Recovery (HMR): End-to-end adversarial learning of human pose and shape. We describe a real time framework for recovering the 3D joint angles and shape of the body from a single RGB image. The first two rowsshow results from our model trained with some 2D-to-3D supervision, the bottom row shows results from a model that is trained in a fully weakly-supervised manner without using any paired 2D-to-3D supervision. We infer the full 3D body even in case of occlusions and truncations. Note that we capture head and limb orientations.

This work addresses the problem of estimating the full body 3D human pose and shape from a single color image. This is a task where iterative optimization-based solutions have typically prevailed, while Convolutional Networks (ConvNets) have suffered because of the lack of training data and their low resolution 3D predictions. Our work aims to bridge this gap and proposes an efficient and effective direct prediction method based on ConvNets. Central part to our approach is the incorporation of a parametric statistical body shape model (SMPL) within our end-to-end framework. This allows us to get very detailed 3D mesh results, while requiring estimation only of a small number of parameters, making it friendly for direct network prediction. Interestingly, we demonstrate that these parameters can be predicted reliably only from 2D keypoints and masks. These are typical outputs of generic 2D human analysis ConvNets, allowing us to relax the massive requirement that images with 3D shape ground truth are available for training. Simultaneously, by maintaining differentiability, at training time we generate the 3D mesh from the estimated parameters and optimize explicitly for the surface using a 3D per-vertex loss. Finally, a differentiable renderer is employed to project the 3D mesh to the image, which enables further refinement of the network, by optimizing for the consistency of the projection with 2D annotations (i.e., 2D keypoints or masks). The proposed approach outperforms previous baselines on this task and offers an attractive solution for direct prediction of 3D shape from a single color image.