Interactive Hand Pose Estimation using a Stretch-Sensing Soft Glove
Interactive Hand Pose Estimation using a Stretch-Sensing Soft Glove
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DOI：
10.1145/3306346.3322957
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发表时间：
2019-07-01
影响因子：
6.2
通讯作者：
Sorkine-Hornung, Olga
中科院分区：
文献类型：
--
作者：
Glauser, Oliver;Wu, Shihao;Sorkine-Hornung, Olga
我们提出了一种拉伸感应软手套，以高精度交互式捕获手部姿势，而不需要外部光学设置。我们演示了如何使用大多数制造实验室中可用的简单工具以低成本制造和校准我们的设备。为了从嵌入在手套中的电容传感器重建姿态，我们提出了一种利用传感器本身空间布局的深度网络架构。该网络只训练一次，使用廉价的现成的手部姿势重建系统来收集训练数据。每个用户的校准然后只使用手套进行实时校准。该手套的功能在一系列烧蚀实验中得到了验证，探索了不同的模型和校准方法。与商用数据手套相比，我们的重建精度提高了35%。
We propose a stretch-sensing soft glove to interactively capture hand poses with high accuracy and without requiring an external optical setup. We demonstrate how our device can be fabricated and calibrated at low cost, using simple tools available in most fabrication labs. To reconstruct the pose from the capacitive sensors embedded in the glove, we propose a deep network architecture that exploits the spatial layout of the sensor itself. The network is trained only once, using an inexpensive off-the-shelf hand pose reconstruction system to gather the training data. The per-user calibration is then performed on-the-fly using only the glove. The glove's capabilities are demonstrated in a series of ablative experiments, exploring different models and calibration methods. Comparing against commercial data gloves, we achieve a 35% improvement in reconstruction accuracy.