Purpose: Currently, a common method of reconstructing mannequin is based on the body measurements or body features, which only preserve the body size lacking of the accurate body geometric shape information. However, the same human body measurement does not equal to the same body shape. This may result in an unfit garment for the target human body. The purpose of this paper is to propose a novel scanning-based pipeline to reconstruct the personalized mannequin, which preserves both body size and body shape information. Design/methodology/approach: The authors first capture the body of a subject via 3D scanning, and a statistical body model is fit to the scanned data. This results in a skinned articulated model of the subject. The scanned body is then adjusted to be pose-symmetric via linear blending skinning. The mannequin part is then extracted. Finally, a slice-based method is proposed to generate a shape-symmetric 3D mannequin. Findings: A personalized 3D mannequin can be reconstructed from the scanned body. Compared to conventional methods, the method can preserve both the size and shape of the original scanned body. The reconstructed mannequin can be imported directly into the apparel CAD software. The proposed method provides a step for digitizing the apparel manufacturing. Originality/value: Compared to the conventional methods, the main advantage of the authors’ system is that the authors can preserve both size and geometry of the original scanned body. The main contributions of this paper are as follows: decompose the process of the mannequin reconstruction into pose symmetry and shape symmetry; propose a novel scanning-based pipeline to reconstruct a 3D personalized mannequin; and present a slice-based method for the symmetrization of the 3D mesh.

Due to the similarities between cashmere and wool, the automatic identification of these two animal fibers continues to be a huge challenge in textile society. In this paper, for the identification of micrographs of cashmere and wool, bag-of-words and spatial pyramid matching are used. Each fiber image was regarded as a collection of feature vectors in our logic. The vectors, extracted from the original dataset, were fed into a support vector machine for supervised classification. The codebook size and the resolution level were completely investigated. The experimental results indicated that the image segmentation delivered a positive contribution in enhancing the accuracy of classification. The overall performance of the model was robust under various blend ratios. It verifies that the bag-of-words with spatial pyramid match is an effective approach to the identification of cashmere and wool fibers.

Image-based garment transfer systems aim to swap the desired clothes from a model to arbitrary users. However, existing works cannot provide the capacity for users to try on various fashion articles according to their wishes, i.e., users can decide which article (e.g., tops, pants or both) to be swapped. In this paper, we propose an Inpainting-based Virtual Try-On Network (I-VTON) which allows the user to try on arbitrary clothes from the model image in a selective manner. To realize the selectivity, we reshape the virtual try-on as a task of image inpainting. Firstly, the texture from the garment and the user are extracted respectively to form a coarse result. In this phase, users can decide which clothes they hope to try on via an interactive texture control mechanism. Secondly, the missing regions in the coarse result are recovered via a Texture Inpainting Network (TIN). We introduce a triplet training strategy to ensure the naturalness of the final result. Qualitative and quantitative experimental results demonstrate that I-VTON outperforms the state-of-the-art methods on both the garment details and the user identity. It is also confirmed our approach can flexibly transfer the clothes in a selective manner.

In this paper, a system with six depth cameras was built to scan both feet simultaneously. An improved calibration method based on a T-shaped checkerboard was used to calculate the extrinsic parameters of the cameras. T-shaped virtual checkerboards were introduced to further fine-tune the accuracy of calibration based on the iterative closest point algorithm. Based on the proposed foot scanner, a complete procedure was introduced to measure the foot automatically by locating the anatomical landmarks without manual intervention. Various experiments were presented to validate the performance of the scanner and the measurements. The results verified that the proposed methods were efficient and versatile for three-dimensional foot scanning and measurement.

Purpose: The automatic body measurement is the key of tailoring, mass customization and fit/ease evaluation. The major challenges include finding the landmarks and extracting the sizes accurately. The purpose of this paper is to propose a new method of body measurement based on the loop structure. Design/methodology/approach: The scanned human model is sliced equally to layers consist of various shapes of loops. The semantic feature analysis has been regarded as a problem of finding the points of interest (POI) and the loop of interest (LOI) according to the types of loop connections. Methods for determining the basic landmarks have been detailed. Findings: The experimental results validate that the proposed methods can be used to locate the landmarks and to extract sizes on markless human scans robustly and efficiently. Originality/value: With the method, the body measurement can be quickly performed with average errors around 0.5 cm. The results of segmentation, landmarking and body measurements also validate the robustness and efficiency of the proposed methods.

In this article, we presented a new automatic three-dimensional-scanned garment fitting method for A-Pose-scanned human models. Both the garment and the human body were decomposed based on feature lines defined by various landmarks. The patches of the three-dimensional garment were automatically positioned around the human model by setting up the correspondence via feature matching. Virtual sewing was engaged to obtain the final results of virtual dressing. The penetration between cloth model and human model was solved by a geometrical method constrained by Laplacian-based deformation. The experimental results indicated that the proposed method was an efficient way for redressing various garments onto various human models while maintaining the original geometrical features of garments.