论文标题
抓取：全身人类对象的数据集
GRAB: A Dataset of Whole-Body Human Grasping of Objects
论文作者
论文摘要
训练计算机以理解，建模和合成人掌握，需要一个包含复杂3D对象形状，详细的接触信息，手动姿势和形状以及3D身体运动的丰富数据集随时间推移。虽然“抓握”通常被认为是一只手稳定地抬起物体，但我们捕获了整个身体的运动，并采用了“全身grasps”的广义概念。因此，我们收集了一个新的数据集，称为Grab（用身体抓握动作），全身抓取，其中包含完整的3D形状和10个受试者的姿势序列，这些受试者与51个具有不同形状和大小的日常对象相互作用。给定MOCAP标记，我们适合完整的3D身体形状和姿势，包括铰接的脸部和手，以及3D物体的姿势。这提供了详细的3D网格，随着时间的流逝，我们可以从中计算身体和物体之间的接触。这是一个独特的数据集，它远远超出了现有数据集，用于建模和了解人类如何掌握和操纵对象，其全身如何涉及的对象以及与任务的相互作用如何变化。我们通过示例应用程序说明了Grab的实际价值；我们训练有条件的生成网络GrabNet预测3D手抓地图，以显示未见的3D对象形状。数据集和代码可用于研究目的，网址为https://grab.is.tue.mpg.de。
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de.
