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For a holistic assessment of the interaction between the human body and tight fitted clothing, it is necessary to consider the mechanical properties of the body. Default avatars in CAD software are usually solid and do not take this interaction into account. For this purpose, a solid avatar is converted to a deformable one by using the soft body physics implementation in the simulation program Blender. The fit of a 3D garment on both avatars are compared, which allows a first evaluation of the differences between these approaches.
When wearing compressive garments, the tissue of the human body is altered in relation to its natural shape by the properties of the applied material and by the pattern construction used.
To check the fit of garments, both construction and selected materials can be virtually simulated in 3D on avatars in corresponding CAD programs before fabrication.
The software Blender allows the modelling of an avatar and to generate in respective to the different tissue zones with their specific properties to adjust them with soft body physics according to the testing of real soft tissue but the models in Blender are mainly using linear springs.
Patterns are virtually simulated in 3D CAD programs before production to check the fit. However, achieving lifelike representations of human avatars, especially regarding soft tissue dynamics, remains challenging. This is mainly since conventional avatars in garment CAD programs are simulated with a continuous hard surface and not corresponding to the human physical and mechanical body properties of soft tissue. In the real world, the human body’s natural shape is affected by the contact pressure of tight-fitting textiles. To verify the fit of a simulated garment, the interactions between the individual body shape and the garment must be considered. This paper introduces an innovative approach to digitising the softness of human tissue using 4D scanning technology. The primary objective of this research is to explore the interactions between tissue softness and different compression levels of apparel, exerting pressure on the tissue to capture the changes in the natural shape. Therefore, to generate data and model an avatar with soft body physics, it is essential to capture the deform ability and elasticity of the soft tissue and map it into the modification options for a simulation. To aim this, various methods from different fields were researched and compared to evaluate 4D scanning as the most suitable method for capturing tissue deformability in vivo. In particular, it should be considered that the human body has different deformation capabilities depending on age, the amount of muscle and body fat. In addition, different tissue zones have different mechanical properties, so it is essential to identify and classify them to back up these properties for the simulation. It has been shown that by digitising the obtained data of the different defined applied pressure levels, a prediction of the deformation of the tissue of the exact person becomes possible. As technology advances and data sets grow, this approach has the potential to reshape how we verify fit digitally with soft avatars and leverage their realistic soft tissue properties for various practical purposes.
In recent years, the demand for accurate and efficient 3D body scanning technologies has increased, driven by the growing interest in personalised textile development and health care. This position paper presents the implementation of a novel 3D body scanner that integrates multiple RGB cameras and image stitching techniques to generate detailed point clouds and 3D mesh models. Our system significantly enhances the scanning process, achieving higher resolution and fidelity while reducing the cost, time and effort required for data acquisition and processing. Furthermore, we evaluate the potential use cases and applications of our 3D body scanner, focusing on the textile technology and health sectors. In textile development, the 3D scanner contributes to bespoke clothing production, allowing designers to construct made-to-measure garments, thus minimising waste and enhancing customer satisfaction through fitting clothing. In mental health care, the 3D body scanner can be employed as a tool for body image analysis, providing valuable insights into the psychological and emotional aspects of self-perception. By exploring the synergy between the 3D body scanner and these fields, we aim to foster interdisciplinary collaborations that drive advancements in personalisation, sustainability, and well-being.
Capturing accurate body dimensions is crucial for the apparel industry, particularly in the creation of customized garments and ensuring an optimal fit. The current standard methods include using measuring tapes on key body areas or employing 3D scanners. Both techniques require the individual to wear tightly fitted clothing or no clothing at all to ensure accurate measurements. These inconveniences may be alleviated with so-called Microwave Imaging (MI), a lesser known imaging technology typically employed in security scanning. MI offers an alternative approach as it is capable of capturing body dimensions accurately and rapidly even under clothing. In this inital work, MI is thoroughly investigated and compared both visually and quantitatively to established measurement techniques. For a quantitative assessment, approximate methods for determining human body dimensions based on MI are presented and compared to aforementioned conventional measurements methods. All scanning procedures were conducted on a static mannequin, being tested in two distinct configurations: unclothed and wearing a jacket. The experimental setup is designed to evaluate the effectiveness of capturing body dimensions in general, and in particular through clothing, with a specific focus on exploring the potential applications of MI data in the context of body measurement. To this end, MI demonstrates to be a fast, comfortable and feasible method for measuring body circumferences, rendering it a viable option for use in the clothing industry. The technology shows promise, especially in its ability to capture body dimensions even through thick clothing.
This paper introduces an AI-assisted pattern generator, aimed to simplify garment design by flattening the pattern creation in an automated process from 3D scans for users without knowledge of conventional pattern construction. This garment tool plug-in computerizes the development of scanned persons into 3D shell surface meshes, which are automatically unwrapped into 2D patterns, streamlining the traditionally complex aspects of garment design for novices. The process uses advanced AI algorithms to facilitate the conversion of 3D scans into usable patterns. Machine learning adapts to different garment styles (close-fitting, regular fit and loose-fitting), ensuring a broad applicability, while customization options allow a precise adaption to individual body measurements. This AI-assisted tool enables a wider audience to generate customized garment creation.
Nowadays, soft avatars are used in various fields to simulate the behavior of human soft tissues in different applications. Likewise, they are also utilized in the garment industry in order to achieve a realistic testing of the fit and functionality of tight-fitting clothing. Therefore it is important that avatars in CAD programs for clothing conform to the mechanical properties of human soft tissue. The accuracy of the avatars' properties in simulating the change in shape of human tissue is crucial here, which is caused by the contact pressure that compressive or tight-fitting garments exert onto the body. In this study, Browzwear’s VStitcher soft avatar Sofia was investigated and different body shapes resulting from being influenced by a legging with different levels of negative ease values were compared with non-affected natural avatar body shape. The examination of the soft avatar simulation shows that although a fast estimation of the tissue displacement can be predicted, there are some shape changes limitations compared to the natural behavior of human soft tissue.