Humans excel at grasping objects and manipulating them. Capturing human
grasps is important for understanding grasping behavior and reconstructing it
realistically in Virtual Reality (VR). However, grasp capture - capturing the
pose of a hand grasping an object, and orienting it w.r.t. the object - is
difficult because of the complexity and diversity of the human hand, and
occlusion. Reflective markers and magnetic trackers traditionally used to
mitigate this difficulty introduce undesirable artifacts in images and can
interfere with natural grasping behavior. We present preliminary work on a
completely marker-less algorithm for grasp capture from a video depicting a
grasp. We show how recent advances in 2D hand pose estimation can be used with
well-established optimization techniques. Uniquely, our algorithm can also
capture hand-object contact in detail and integrate it in the grasp capture
process. This is work in progress, find more details at this https URL
cc.gatech.edu/grasp_capture.html.

研究了通过使用最新的二维手姿势估计技术和优化技术进行的无标记算法来捕捉手抓握物体的过程，此算法也可以详细地捕捉手 - 物体接触并将其集成进抓握捕捉过程中。