Human behavior and activity understanding through images and video frames have been a viral topic within the field of computer vision. As a significant part, skeleton estimation, which is also called pose estimation, has gained a lot of attention. For pose estimation, most of the deep learning approaches usually focus on the joint feature. However, the joint feature is inadequate and not enough, especially when the image includes multi person with an occluded or not fully visible pose. This thesis work focuses on the implementation and evaluation of a method to estimate 3D pose for multi person in a scene with a single RGB camera by using the hybrid of regression and lifting approach.