BODYFITR: Robust Automatic 3D Human Body Fitting

This paper proposes BODYFITR, a fully automatic method to fit a human body model to static 3D scans with complex poses. Automatic and reliable 3D human body fitting is necessary for many applications related to healthcare, digital ergonomics, avatar creation and security, especially in industrial contexts for large-scale product design. Existing works either make prior assumptions on the pose, require manual annotation of the data or have difficulty handling complex poses. This work addresses these limitations by providing a novel automatic fitting pipeline with carefully integrated building blocks designed for a systematic and robust approach. It is validated on the 3DBodyTex dataset, with hundreds of high-quality 3D body scans, and shown to outperform prior works in static body pose and shape estimation, qualitatively and quantitatively. The method is also applied to the creation of realistic 3D avatars from the high-quality texture scans of 3DBodyTex, further demonstrating its capabilities

Computational Design of Wiring Layout on Tight Suits with Minimal Motion Resistance

An increasing number of electronics are directly embedded on the clothing to monitor human status (e.g., skeletal motion) or provide haptic feedback. A specific challenge to prototype and fabricate such a clothing is to design the wiring layout, while minimizing the intervention to human motion. We address this challenge by formulating the topological optimization problem on the clothing surface as a deformation-weighted Steiner tree problem on a 3D clothing mesh. Our method proposed an energy function for minimizing strain energy in the wiring area under different motions, regularized by its total length. We built the physical prototype to verify the effectiveness of our method and conducted user study with participants of both design experts and smart cloth users. On three types of commercial products of smart clothing, the optimized layout design reduced wire strain energy by an average of 77% among 248 actions compared to baseline design, and 18% over the expert design.Comment: This work is accepted at SIGGRAPH ASIA 2023(Conference Track

Parametric design for human body modeling by wireframe-assisted deep learning

Statistical learning of human body shape can be used for reconstructing or estimating body shapes from incomplete data, semantic parametric design, modifying images and videos, or simulation. A digital human body is normally represented in a high-dimensional space, and the number of vertices in a mesh is far larger than the number of human bodies in public available databases, which results in a model learned by Principle Component Analysis (PCA) can hardly reflect the true variety in human body shapes. While deep learning has been most successful on data with an underlying Euclidean or grid-like structure, the geometric nature of human body is non-Euclidean, it will be very challenging to perform deep learning techniques directly on such non-Euclidean domain. This paper presents a deep neural network (DNN) based hierarchical method for statistical learning of human body by using feature wireframe as one of the layers to separate the whole problem into smaller and more solvable sub-problems. The feature wireframe is a collection of feature curves which are semantically defined on the mesh of human body, and it is consistent to all human bodies. A set of patches can then be generated by clustering the whole mesh surface to separated ones that interpolate the feature wireframe. Since the surface is separated into patches, PCA only needs to be conducted on each patch but not on the whole surface. The spatial relationships between the semantic parameter, the wireframe and the patches are learned by DNN and linear regression respectively. An application of semantic parametric design is used to demonstrate the capability of the method, where the semantic parameters are linked to the feature wireframe instead of the mesh directly. Under this hierarchy, the feature wireframe acts like an agent between semantic parameters and the mesh, and also contains semantic meaning by itself. The proposed method of learning human body statistically with the help of feature wireframe is scalable and has a better quality

Lagrangian-on-Lagrangian Garment Design

Since the discovery of elastomeric materials, such as spandex or lycra, skintight clothing has revolutionized many different areas of the clothing industry, such as body-shaping clothing, athletic wear, and medical garments, among others. Often, this kind of clothing is designed to fulfill a given purpose, such as providing comfort, mobility, or improving recovery in the case of an athlete, provide support or exert some desired pressure in the case of medical garments, or actively deform the body to acquire some desired shape. Additionally, some designs aim to improve the life of the garment by, for example, minimizing tractions across the seams. While many tight-skin garments are sold in the market for generic body shapes, many of the purposes here mentioned are only achievable through a personalized fitting. To this end, we introduce a novel model, where the cloth is modeled as a membrane, parameterized as a function of the body. The cloth, is then able to slide on the body and deform it while staying always in contact. We call this model Lagrangian-on-Lagrangian. Based on this model, we develop an optimization framework, based on sensitivity analysis, capable of developing sewable patterns such that, when worn by a person, satisfy a given design target. With the framework, we include several design targets such as, body shape, stretch, pressure, sliding under motion, and seam traction. We evaluate our method on a variety of applications, as well as body shapes