Recently convolutional neural networks (CNNs) have been employed to address the problem of hand pose estimation. In this work, we introduce an end-to-end deep architecture that can accurately estimate hand pose through the joint use of model-based and fine-tuning methods. In the model-based stage, we make use of the prior information in hand model geometry to ensure the geometric validity of the estimated poses. Next, we introduce a fine-tuning approach that learns to refine the errors between the model and observed hand. Our approach is validated on three challenging public datasets and achieves state-of-the-art performance.

中文翻译：

---

用于实时手部姿势估计的CNN模型

最近，卷积神经网络 (CNN) 已被用于解决手部姿势估计问题。在这项工作中，我们引入了一种端到端的深度架构，该架构可以通过联合使用基于模型的方法和微调方法来准确估计手部姿势。在基于模型的阶段，我们利用手模型几何中的先验信息来确保估计姿态的几何有效性。接下来，我们介绍了一种微调方法，该方法可以学习改进模型和观察到的手之间的误差。我们的方法在三个具有挑战性的公共数据集上得到验证，并实现了最先进的性能。