Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of two main modules. The former presents a segmentation-based approach to detect the hand skeleton and localize the hand bounding box. The second module regresses the 2D joint locations through a multi-scale heatmap regression approach that exploits the predicted hand skeleton as a constraint to guide the model. Furthermore, we construct a new dataset that is suitable for both hand detection and pose estimation. We qualitatively and quantitatively validate our method on two datasets. Results demonstrate that the proposed method outperforms state-of-the-art and can recover the pose even in cluttered images and complex poses.

翻译：现有基于 RGB 的 2D 手表代表了从单一分辨率中学习联合位置的估算方法,而单一分辨率不适合不同手尺寸。为了解决这一问题,我们提议了一个新的深层次学习框架,由两个主要模块组成。前者提出了一种基于分解的方法,以探测手骨骼并使手框本地化。第二个模块通过多尺度的热映射回归法,将2D 联合位置倒退,将预测的手骨架作为指导模型的制约因素。此外,我们建造了一套新的数据集,既适合手检,又适合作出估计。我们在两个数据集中从质量和数量上验证了我们的方法。结果显示,拟议方法的形状超越了工艺状态,即使以杂乱的图像和复杂的外形形式,也可以恢复这种形态。