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

Geometry And Topology: Building Machine Learning Surrogate Models With Graphic Statics Method

This dissertation aims at developing a machine learning workflow in solving design-related problems, taking a data-driven structural design method with topological data using graphic statics as an example. It shows the advantages of building machine learning surrogate models for learning the design topology -- the relationship of design elements. It reveals a future tendency of the coexistence of the human designer and the machine, in which the machine learns the appearance and correlation between design data, while the human supervises the learning process. Theoretically, with the commencement of the age of Big Data and Artificial Intelligence, the usage of machine learning in solving design problems is widely applied. The existing research mainly focuses on the machine learning of the geometric data, however, the internal logic of a design is represented as the topology, which describes the relationship between each design element. The topology can not be easily represented for the human designer to understand, however it\u27s readable and understandable by the machine, which suggests a method of using machine learning techniques to learn the intrinsic logic of a design as the topology. Technically, we propose to use machine learning as a framework and graphic statics as a supporting method to provide training data, suggesting a new design methodology by the machine learning of the topology. Different from previous geometry-based design, in which only the design geometry is presented and considered, in this new topology-based design, the human designer employs the machine and provides training materials showing the topology of a design to train the machine. The machine finds the design rules related to the topology and applies the trained machine learning models to generate new design cases as both the geometry and the topology