# Towards Markerless Grasp Capture

## TL;DR

This paper introduces a preliminary markerless method for capturing human grasp poses from video, leveraging recent 2D hand pose estimation advances and detailed hand-object contact modeling, aiming to improve realism in VR applications.

## Contribution

It presents a novel markerless grasp capture algorithm that combines 2D hand pose estimation with contact modeling, advancing beyond traditional marker-based methods.

## Key findings

- Successfully captures hand-object contact details
- Integrates 2D pose estimation with grasp modeling
- Works in a markerless, video-based setting

## Abstract

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.