Relative pose estimation for human scans enjoys a promising prospect. However, most existing methods mainly focus on indoor or outdoor scenes, requiring considerable overlap between the inputs. We present a technique for estimating the relative pose whatever the overlap between the human RGBD input scans is. For non-overlapping scans, the insight is to take advantage of the underlying human geometry prior as much as possible. We utilize the implicit function model for human reconstruction, enriching abundant hidden cues for unseen regions, then we use the completed human geometry to get a stable pose estimation. Our evaluation shows that our approach outperforms considerably than standard pipelines in non-overlapping setting, without compromising performance over overlapping input scans.