Abstract: ‍ ‍The point clouds obtained by 3D scanning equipment have problems， such as target occlusion， capture perspective， etc.， resulting in the lack of point clouds. Point cloud completion is a significant work for downstream tasks. However， the existing point cloud completion work focuses on the completion of simple objects， and there is a lack of research on point cloud completion of complex human body. In order to introduce the point cloud completion task into the downstream human reconstruction， this paper proposed a coarse to fine human point cloud prediction method to reconstruction human point cloud position and color. The method in this paper created a human point cloud dataset with fine clothing texture， adopted the network structure of point cloud migration， made full use of the global rough information of point cloud， and combined multi-layer network to predict the complete point cloud， so as to optimize the up-sampling scheme； Then， the color was completed on the point cloud after up-sampling， and the eigenvalues were diffused and extracted through part of the point cloud color information； Finally， a multi-layer perceptron was used for color prediction. The experimental results on human dataset shows that this method not only has better performance in objective indicators， but also can ensure a more complete and clear edge point cloud in subjective quality compared with the state-of-the-art methods.