# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 2 Related Work
### 3D Hand Pose Estimation Based on RGB Images  
Due to the lack of depth information, hand pose estimation based on RGB images, especially single RGB images, developed relatively late. The accuracy of the RGB-based method is not as good as that of the depth-based method. However, an RGB image is easier to obtain, and the equipment is cheaper. Thus, an increasing amount of research focusses on the RGB-based method.  
[PERSON] [10] proposed estimating poses based on multivision and using binocular vision to restore the exact distance information and realized RGB-based hand pose estimation. However, this method still places a large number of constraints on users. In 2017, [PERSON] [16] realized 3D hand pose estimation through a single RGB image based on deep learning; the method used deep networks to learn reasonable prior information from the data to solve the fuzzy problem without relying on any special equipment. A feasible network framework for deriving 3D keypoints from 2D keypoints was generated. The method consisted of three deep networks: the first network performed hand segmentation to locate the hand in the image, the second network estimated the 2D keypoint score map from the output of the first network using convolutional pose machines (CPMs) [17], and the third network derived a 3D keypoint from the 2D keypoints. Furthermore, the method proposed a normalized coordinate system that regarded the hand position in the normalized coordinate system as a rotation in the camera coordinate system; the hand position was calculated in the normalized coordinate system, and the rotation angle was calculated by using the neural network to restore the position of the 3D keypoint. This method was the first to achieve 3D hand pose estimation with a single RGB image. [PERSON] [18] used a variational encoder hand pose estimation method to project the image and keypoint information onto the hidden space and optimized the accuracy by minimizing the distance between the image and the information in the hidden space. [PERSON] [19] used weakly supervised learning to estimate hand poses. This method does not directly perform supervision training through the three-dimensional hand keypoints, but rather generates depth images of the estimated 3D hand pose through a GAN. [PERSON] [20] restored occluded hand areas through a GAN, which solved the problem of hand area occlusion to a certain extent.  
Of the computer-vision-based hand pose estimation methods, the depth-based method requires more expensive equipment, but the multivision-based method still places certain constraints on the user. Most methods based on a single RGB image use a 2D score map but ignore the information contained in the RGB image. Based on 3D hand estimation from a single RGB image, we propose a method that uses the attention mechanism to fuse the 2D score map and the RGB image channel features. In Section 3, we introduce the prior methods and our methods. In Section 4, we introduce our experimental dataset and compare it with the baseline in the dataset and with the state-of-the-art methods. In Section 5, we summarize our paper.