We present EMDB, the Electromagnetic Database of Global 3D Human Pose and Shape in the Wild. EMDB is a novel dataset that contains high-quality 3D SMPL pose and shape parameters with global body and camera trajectories for in-the-wild videos. We use body-worn, wireless electromagnetic (EM) sensors and a hand-held iPhone to record a total of 58 minutes of motion data, distributed over 81 indoor and outdoor sequences and 10 participants. Together with accurate body poses and shapes, we also provide global camera poses and body root trajectories. To construct EMDB, we propose a multi-stage optimization procedure, which first fits SMPL to the 6-DoF EM measurements and then refines the poses via image observations. To achieve high-quality results, we leverage a neural implicit avatar model to reconstruct detailed human surface geometry and appearance, which allows for improved alignment and smoothness via a dense pixel-level objective. Our evaluations, conducted with a multi-view volumetric capture system, indicate that EMDB has an expected accuracy of 2.3 cm positional and 10.6 degrees angular error, surpassing the accuracy of previous in-the-wild datasets. We evaluate existing state-of-the-art monocular RGB methods for camera-relative and global pose estimation on EMDB. EMDB is publicly available under https://ait.ethz.ch/emdb

翻译：我们提出EMDB（Electromagnetic Database of Global 3D Human Pose and Shape in the Wild），这是一个新颖的野外视频数据集，包含高质量3D SMPL人体姿态与形状参数，以及全局身体与相机轨迹。我们采用穿戴式无线电磁传感器与手持iPhone，在81个室内外序列中采集了10位参与者共计58分钟的运动数据。除精确的人体姿态与形状外，本数据集还提供全局相机位姿与人体根轨迹。为构建EMDB，我们提出多阶段优化流程：首先将SMPL模型拟合至6自由度电磁测量值，随后通过图像观测细化姿态。为获得高质量结果，我们利用神经隐式化身模型重建精细的人体表面几何与外观，通过密集像素级目标实现更优的对齐和平滑性。基于多视角体积捕捉系统的评估表明，EMDB的预期位置误差为2.3厘米、角度误差为10.6度，精度超越现有野外数据集。我们评估了现有最先进单目RGB方法在EMDB上的相机相对与全局姿态估计性能。EMDB已公开于https://ait.ethz.ch/emdb。