# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 2 Related Work
### 3D Hand Pose Estimation Based on Depth Images  
Traditional computer-based 3D hand pose estimation uses depth images. Depth images include depth information, which is helpful for obtaining the distances between keypoints.  
[PERSON] et al. [1] proposed a preliminary positioning and optimization method, HandDeep, based on a convolutional neural network. It can accurately locate a hand in a single depth image after training with multiple labeled depth images. In 2016, [PERSON] et al. [2] proposed an acceleration method using matrix completion, and it can be applied to large-scale, real-time hand pose estimation without relying on a GPU. In 2017, [PERSON] et al. [3] optimized several aspects of the network and the training process and improved the accuracy. The method included data expansion, dropout, the addition of residual modules, and the optimization of hand segmentation. In 2014, [PERSON] et al. [4] proposed a hand pose recognition method that combines the generation method and the data-driven method. In 2016, [PERSON] et al. [5] proposed an algorithm for matching depth images with models. By constructing a hand model, this method matched the keypoints from the palm to fingertip. [PERSON] et al. [11] proposed a method for dense pixels that aggregated local estimates using nonparametric mean shift variables, explicitly forcing consistency between the estimated 3D joint coordinates and the 2D and 3D local estimations. This approach provided a better fusion between 2D detection  
Figure 1: Our task. The 3D hand pose is estimated by an end-to-end convolutional neural network (CNN). The input is an RGB image, and the output is the 3D coordinate of each keypoint on the hand.  
and 3D regression than prior mechanisms and various baselines. In 2018, [PERSON] et al. [12] set the gesture segmentation under the first-person perspective and the presence of occlusion with the aid of a conditional random field (CRF). For the first time, a method performed hand segmentation and detection from a self-centered perspective and under occlusion, and the hand pose estimation accuracy was improved by improving the segmentation accuracy. However, this method still did not solve the problem of occluded objects or the similarity between background objects and hands in RGB images. Motivated by CycleGAN [13], [PERSON] et al. [14] proposed a method for expanding datasets. This method can actively generate keypoint data by training datasets and restore depth images through a GAN (Generative Adversarial Networks) after CycleGAN training. To some extent, the lack of training data for partial perspectives was solved. The proposed solution was useful to some extent. However, a complicated cyclical relationship was used, which made the training process cumbersome and the network complicated. [PERSON] et al. [15] matched depth images to bone images based on a hidden space transformation. Although the accuracy was mediocre, the method could achieve a speed of 90 frames per second (FPS) on a CPU, improving the efficiency of realizing image-based hand pose estimation. The method mapped paired depth and bone images to the same position in the hidden space and restored the original image from the hidden space via deconvolution. The depth-image-based pose estimation method has gradually matured, but the depth acquisition device, which is sensitive to illumination, jitter, and distance, imposes constraints on the user. In addition, it is expensive.