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Automatic 3D human body measurement is a crucial issue for tailoring and made-to-measure. This paper presents a novel framework for 3D human landmarks extraction and measurement. The proposed approach first segments the 3D human body into 13 parts by utilizing an improved Mean Curvature Skeleton (MCS) algorithm, in which we modify the Laplacian operator used in the original MCS with mesh saliency to enable the segmentation boundaries to be closer to the human joints. Based on the human segmentation, K-Nearest Neighbors, linear modeling, and geometric methods are employed to extract at least 21 landmarks. Many essential landmarks, such as acromion, elbow, crotch, etc., are extracted in new ways. To our best knowledge, this is the first paper to propose a generalized solution to approximate the elbow point for arbitrary arm poses in the automatic 3D human measurement systems. Subsequently, various anthropometric measurements can be calculated according to the landmarks extracted automatically. The proposed method is validated on the public datasets and our real scans, and the experimental results have verified that the proposed approach is efficient and effective in processing various 3D human bodies.

Automatic 3D human body landmarks extraction and measurement based on mean curvature skeleton for tailoring