# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 1 Introduction  
Gesture estimation plays a significant role in computer science, and related tasks aim toward understanding human gestures through algorithms. It is robust to environment changes such as mutative shooting distance and glare light. Human-computer interaction (HCI) can be implemented wherever and whenever, has fewer constraints, and enables computers to efficiently and precisely understand user commands without any mechanical assistance. Gestures for HCI are quick, vivid, intuitive, flexible, and visual; they can enable soundless interactions and bridge the gap between the real world and virtual worlds.  
To recognize gestures, 3D hand poses are required. Computer-vision-based hand pose estimation enables people to communicate with machines more naturally. With the development of computer vision, pose estimation no longer relies on traditional wearable devices in specific scenes but can be directly implemented based on image recognition. The research on pose estimation in computer vision includes three main categories: depth images, multivision RGB images, and single RGB images. Many studies have estimated hand poses through depth images [1; 2; 3; 4; 5; 6; 7; 8; 9] and achieved good results. However, depth images must be obtained using indoor depth cameras and are thus not as convenient as RGB images. Multivision has successfully achieved hand tracking and hand pose estimation through RGB images [10] but there are still some constraints on users due to the requirements of multivision.  
The goal of hand pose estimation based on computer vision is to free users from the constraints of depth equipment and multivision images and to facilitate HCI through mobile phones and other devices. Therefore, it is of great significance to be able to estimate hand poses based on a single RGB image, which allows full 3D hand poses to be learned from a single RGB image and does not rely on any special equipment or environment.  
At present, estimating 3D hand poses from 2D score maps is the most commonly used pose estimation method based on single RGB images. Although some methods for human body posture estimation can turn RGB images into 3D postures, they cannot be directly applied to hand pose estimation. Moreover, hands have a more serious self-shielding problem than other parts of the human body as the inside of each hand is asymmetrical while the human body is symmetrical. A recent hand pose estimation method based on a single RGB image first estimates the hand state and the rotation angle relative to the camera, and then calculates the 3D coordinates. Unfortunately, for this method, the estimation is based on only 2D keypoints, and the texture features of the RGB image are ignored. In this paper, a fusing channel attention method is introduced to combine a 2D pose and an RGB image to estimate a 3D pose, which effectively solves the problem of different types of input data. The input is an RGB image of a human hand. After applying the end-to-end neural network, we obtain a 3D array. The 3D array is the spatial location of the 21 keypoints of the hand in the input image, as shown in Figure 1.