Towards Markerless Grasp Capture
This work addresses the need for realistic grasp reconstruction in VR by eliminating artifacts from markers, though it is incremental as it builds on existing techniques.
The paper tackles the problem of capturing human hand grasps without markers, which is difficult due to hand complexity and occlusion, by developing a markerless algorithm from video that also captures hand-object contact. The result is a preliminary method that integrates 2D hand pose estimation with optimization techniques, but no concrete performance numbers are provided.
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 https://contactdb. cc.gatech.edu/grasp_capture.html.