Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Existing methods still struggle in challenging poses where the human body is highly occluded or is closely interacting with the scene. To address this issue, we propose a scene-aware egocentric pose estimation method that guides the prediction of the egocentric pose with scene constraints. To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network. Next, we propose a scene-aware pose estimation network that projects the 2D image features and estimated depth map of the scene into a voxel space and regresses the 3D pose with a V2V network. The voxel-based feature representation provides the direct geometric connection between 2D image features and scene geometry, and further facilitates the V2V network to constrain the predicted pose based on the estimated scene geometry. To enable the training of the aforementioned networks, we also generated a synthetic dataset, called EgoGTA, and an in-the-wild dataset based on EgoPW, called EgoPW-Scene. The experimental results of our new evaluation sequences show that the predicted 3D egocentric poses are accurate and physically plausible in terms of human-scene interaction, demonstrating that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.

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。

无标记的单眼3D人类运动捕获（MOCAP）与场景相互作用是一个充满挑战的研究主题，与扩展现实，机器人技术和虚拟头像生成有关。由于单眼环境的固有深度歧义，使用现有方法捕获的3D运动通常包含严重的人工制品，例如不正确的身体场景互穿，抖动和身体漂浮。为了解决这些问题，我们提出了HULC，这是一种新的3D人类MOCAP方法，它知道场景几何形状。 HULC估计3D姿势和密集的身体环境表面接触，以改善3D定位以及受试者的绝对尺度。此外，我们基于新的姿势歧管采样，引入了3D姿势轨迹优化，该采样解决了错误的身体环境互穿。尽管所提出的方法与现有场景感知的单眼MOCAP算法相比需要较少的结构化输入，但它会产生更加可行的姿势：HULC显着且一致地在各种实验和不同指标上都优于现有方法。项目页面：https：//vcai.mpi-inf.mpg.de/projects/hulc/。

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。

对象检测在清洁数据集上取得了有希望的性能，但仍然探讨了如何在对抗性鲁棒性和清洁精度之间实现更好的权衡。对抗性培训是提高稳健性的主流方法，但大多数作品将牺牲清洁精度，以获得比标准训练的坚固性。在本文中，我们提出了统一的解耦特征对准（UDFA），一种新型微调范例，通过完全探索对象检测的自我知识蒸馏和对抗训练之间的组合来实现比现有方法更好的性能。我们首先使用分离的前/后地特征来构建自我知识蒸馏分支，从预磨牙探测器（作为教师）和来自学生探测器的对抗特征表示之间的清洁特征表示之间。然后我们通过将原始分支解耦为自我监督的学习分支和新的自我知识蒸馏分支来探索自我知识蒸馏。通过对Pascal-VOC和MS-Coco基准测试的广泛实验，评估结果表明，UDFA可以超越标准培训和最先进的对抗对象培训方法进行对象检测。例如，与教师探测器相比，我们在GFLV2与RESET-50的方法通过Pascal-Voc上的2.2 AP提高了干净精度;与SOTA对抗性培训方法相比，我们的方法通过1​​.6 AP改善了干净的精度，同时通过0.5 AP改善对抗性鲁棒性。我们的代码将在https://github.com/grispeut/udfa提供。

InputOutput Input Output Fig. 1. Unlike current face reenactment approaches that only modify the expression of a target actor in a video, our novel deep video portrait approach enables full control over the target by transferring the rigid head pose, facial expression and eye motion with a high level of photorealism.We present a novel approach that enables photo-realistic re-animation of portrait videos using only an input video. In contrast to existing approaches that are restricted to manipulations of facial expressions only, we are the irst to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor. The core of our approach is a generative neural network with a novel space-time architecture. The network takes as input synthetic renderings of a parametric face model, based on which it predicts photo-realistic video frames for a given target actor. The realism in this rendering-to-video transfer is achieved by careful adversarial training, and as a result, we can create modiied target videos that mimic the behavior of the synthetically-created input. In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network ś thus taking full control of the

We propose a CNN-based approach for 3D human body pose estimation from single RGB images that addresses the issue of limited generalizability of models trained solely on the starkly limited publicly available 3D pose data. Using only the existing 3D pose data and 2D pose data, we show state-of-the-art performance on established benchmarks through transfer of learned features, while also generalizing to in-the-wild scenes. We further introduce a new training set for human body pose estimation from monocular images of real humans that has the ground truth captured with a multi-camera marker-less motion capture system. It complements existing corpora with greater diversity in pose, human appearance, clothing, occlusion, and viewpoints, and enables an increased scope of augmentation. We also contribute a new benchmark that covers outdoor and indoor scenes, and demonstrate that our 3D pose dataset shows better in-the-wild performance than existing annotated data, which is further improved in conjunction with transfer learning from 2D pose data. All in all, we argue that the use of transfer learning of representations in tandem with algorithmic and data contributions is crucial for general 3D body pose estimation.

Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on monocular and few research on stereo event vision. In this paper, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images and inertial measurements. Our proposed pipeline achieves temporal tracking and instantaneous matching between consecutive stereo event streams, thereby obtaining robust state estimation. In addition, the motion compensation method is designed to emphasize the edge of scenes by warping each event to reference moments with IMU and ESVIO back-end. We validate that both ESIO (purely event-based) and ESVIO (event with image-aided) have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. A real-world large-scale experiment is also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments.

This paper studies audio-visual suppression for egocentric videos -- where the speaker is not captured in the video. Instead, potential noise sources are visible on screen with the camera emulating the off-screen speaker's view of the outside world. This setting is different from prior work in audio-visual speech enhancement that relies on lip and facial visuals. In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression. We compare object recognition and action classification based visual feature extractors, and investigate methods to align audio and visual representations. Then, we examine different fusion strategies for the aligned features, and locations within the noise suppression model to incorporate visual information. Experiments demonstrate that visual features are most helpful when used to generate additive correction masks. Finally, in order to ensure that the visual features are discriminative with respect to different noise types, we introduce a multi-task learning framework that jointly optimizes audio-visual noise suppression and video based acoustic event detection. This proposed multi-task framework outperforms the audio only baseline on all metrics, including a 0.16 PESQ improvement. Extensive ablations reveal the improved performance of the proposed model with multiple active distractors, over all noise types and across different SNRs.

事件摄像机是运动激活的传感器，可捕获像素级照明的变化，而不是具有固定帧速率的强度图像。与标准摄像机相比，它可以在高速运动和高动态范围场景中提供可靠的视觉感知。但是，当相机和场景之间的相对运动受到限制时，例如在静态状态下，事件摄像机仅输出一点信息甚至噪音。尽管标准相机可以在大多数情况下，尤其是在良好的照明条件下提供丰富的感知信息。这两个相机完全是互补的。在本文中，我们提出了一种具有鲁棒性，高智能和实时优化的基于事件的视觉惯性镜（VIO）方法，具有事件角度，基于线的事件功能和基于点的图像功能。提出的方法旨在利用人为场景中的自然场景和基于线路的功能中的基于点的功能，以通过设计良好设计的功能管理提供更多其他结构或约束信息。公共基准数据集中的实验表明，与基于图像或基于事件的VIO相比，我们的方法可以实现卓越的性能。最后，我们使用我们的方法演示了机上闭环自动驾驶四极管飞行和大规模室外实验。评估的视频在我们的项目网站上介绍：https：//b23.tv/oe3qm6j