# RepNet: A Lightweight Human Pose Regression Network Based on Re-Parameterization
## 1 Introduction  
Human pose estimation [1; 2; 3] refers to the locating of the keypoints of each part of the human body and the reconstructing the human joints and limb stems in images or videos, and it can also be regarded as searching for a specific pose in a space composed of all joint poses. With the development of deep learning, human pose estimation has become an important way [4; 5; 6] to solve the problems of image analysis and video understanding, and its research results have been utilized in a variety of fields, such as entertainment, sports, apparel, animation, and medicine. Human pose estimation is categorized into 2D pose estimation [7; 8; 9] and 3D pose estimation [3], according to the space to be solved. Two-dimensional pose estimation deals with image or video frames containing multiple human bodies to determine the 2D coordinates of keypoints, and there are two ways of thinking to solve this problem. In the bottom-up approach [10; 11; 12], all keypoints are first detected and then assembled into actual poses using a matching algorithm. In contrast, in the top-down approach [13; 14; 15], the human body is first detected with a detector and cropped to an appropriate size, and the coordinates of the keypoints for each human body are predicted in a two-stage keypoint estimation network. In this work, we use a top-down approach that focuses on improving the detection of a two-stage single-person position estimation network. The regression-based method [16; 17; 18] was first proposed as shown in Figure 1a, and it uses global mean pooling to reduce the dimensions of the feature graph and gives the predicted keypoint coordinates directly through the linear layer. Although this method is fast and efficient, the processing of the feature maps is too simple, and results in a direct lossof spatial information. Moreover, the constraints between keypoints are not considered in the design of the networks, and these shortcomings limit the development of the regression method. Nevertheless, the regression-based method has great potential, and it is expected that its concept of end-to-end design will become popular in the future. Currently, pose estimation tasks based on heatmap methods [14; 15; 19; 20] have achieved the best results (SOTA). They use heatmap coding to supervise feature extraction and expand the size of the feature map via a deconvolution layer, as shown in Figure 1b. The heatmap method can obtain good spatial information, but it also introduces some disadvantages: the image scaling during heatmap coding will cause coordinate offsets and quantization errors [19; 20]. In addition, to obtain the final prediction value, the non-maximum suppression algorithm is usually used, which makes end-to-end training impossible, and a huge consumption of computing power will be caused by the high-resolution heatmap required during the training. Based on the above point of view, this paper is aimed at putting forward a lightweight human pose estimation model, RepNet, combined with a regression method and the idea of residual likelihood estimation [21]. It is based on re-parameterization [22] technology, and achieves the same reasoning efficiency as the simple model, while ensuring the accuracy of the complex model. The overall design block diagram is shown in Figure 2. In summary, the contributions of this paper are as follows:  
1. In this paper, we propose a lightweight human pose regression framework that uses an end-to-end network design without a post-processing module, and can directly specify the coordinates of keypoints. The multi-parameterized module is adopted to reconstruct the main part of the convolution, while the well-designed training  
Figure 1: Two types of human pose estimation methods. structure is used to improve the training performance. Simplifying the structure of the network also improves the reasoning efficiency;
2. To solve the problem of the loss of precision in a simple model, the advanced idea of residual likelihood estimation is introduced in this paper.