3D Garment Modelling - Creation of a Virtual Mannequin of the Human Body

This work presents the modelling and numerical simulation of mannequins as well as clothes in a 3D virtual environment. The paper explains garment modelling, with the objective of defining the development of a 3D body model that is useful and based on the demand of the garment industry. For this purpose a development strategy should be defined. Many scientists are working on the creative process for virtual garment development to create a garment directly on a 3D model of the human body, also called “ virtual tailoring”.The first strategic issue in this context that we present is that the model must necessarily incorporate the ease garment model associatively. These parameters define the priority concepts that are the draping and proper fit of the garment. The second point is that the transition between the 3D and 2D patterns, known as flattening 3D patterns, must be associative, precise and must take into account the real deformation of the fabric.Project entitled “Development of the research infrastructure of innovative techniques and technologies of the textile garment industry” CLO-2IN-TEX, financed by Operational Program Innovative Economy, 2007-2013, Action 2.

Modelling 3D humans : pose, shape, clothing and interactions

Digital humans are increasingly becoming a part of our lives with applications like animation, gaming, virtual try-on, Metaverse and much more. In recent years there has been a great push to make our models of digital humans as real as possible. In this thesis we present methodologies to model two key characteristics of real humans, their appearance and actions. This thesis covers four innovations: (i) MGN, the first approach to reconstruct 3D garments and body shape underneath, as separate meshes, from a few RGB images of a person. This allows, for the first time, real world applications like texture transfer, garment transfer and virtual try-on in 3D, using just images. (ii) IPNet, a neural network, that leverages implicit functions for detailed reconstruction and registers the reconstructed mesh with the parametric SMPL model to make it controllable for real world tasks like animation and editing. (iii) LoopReg, a novel formulation that makes 3D registration task end-to-end differentiable for the first time. Semi-supervised LoopReg outperforms contemporary supervised methods using ∼100x less supervised data. (iv) BEHAVE the first dataset and method to track full body real interactions between humans and movable objects. All our code, MGN digital wardrobe and BEHAVE dataset are publicly available for further research.Digital humans are increasingly becoming a part of our lives with applications like animation, gaming, virtual try-on, Metaverse and much more. In recent years there has been a great push to make our models of digital humans as real as possible. In this thesis we present methodologies to model two key characteristics of real humans, their appearance and actions. This thesis covers four innovations: (i) MGN, the first approach to reconstruct 3D garments and body shape underneath, as separate meshes, from a few RGB images of a person. This allows, for the first time, real world applications like texture transfer, garment transfer and virtual try-on in 3D, using just images. (ii) IPNet, a neural network, that leverages implicit functions for detailed reconstruction and registers the reconstructed mesh with the parametric SMPL model to make it controllable for real world tasks like animation and editing. (iii) LoopReg, a novel formulation that makes 3D registration task end-to-end differentiable for the first time. Semi-supervised LoopReg outperforms contemporary supervised methods using ∼100x less supervised data. (iv) BEHAVE the first dataset and method to track full body real interactions between humans and movable objects. All our code, MGN digital wardrobe and BEHAVE dataset are publicly available for further research.Der digitale Mensch wird immer mehr zu einem Teil unseres Lebens mit Anwendungen wie Animation, Spielen, virtuellem Ausprobieren, Metaverse und vielem mehr. In den letzten Jahren wurden große Anstrengungen unternommen, um unsere Modelle digitaler Menschen so real wie möglich zu gestalten. In dieser Arbeit stellen wir Methoden zur Modellierung von zwei Schlüsseleigenschaften echter Menschen vor: ihr Aussehen und ihre Handlungen. Wir schlagen MGN vor, den ersten Ansatz zur Rekonstruktion von 3D-Kleidungsstücken und der darunter liegenden Körperform als separate Netze aus einigen wenigen RGB-Bildern einer Person. Wir erweitern das weit verbreitete SMPL-Körpermodell, das nur unbekleidete Formen darstellt, um auch Kleidungsstücke zu erfassen (SMPL+G). SMPL+G kann mit Kleidungsstücken bekleidet werden, die entsprechend dem SMPL-Modell posiert und geformt werden können. Dies ermöglicht zum ersten Mal reale Anwendungen wie Texturübertragung, Kleidungsübertragung und virtuelle Anprobe in 3D, wobei nur Bilder verwendet werden. Wir unterstreichen auch die entscheidende Einschränkung der netzbasierten Darstellung für digitale Menschen, nämlich die Fähigkeit, hochfrequente Details darzustellen. Daher untersuchen wir die neue implizite funktionsbasierte Darstellung als Alternative zur netzbasierten Darstellung (einschließlich parametrischer Modelle wie SMPL) für digitale Menschen. Typischerweise mangelt es den Methoden, die auf letzteren basieren, an Details, während ersteren die Kontrolle fehlt. Wir schlagen IPNet vor, ein neuronales Netzwerk, das implizite Funktionen für eine detaillierte Rekonstruktion nutzt und das rekonstruierte Netz mit dem parametrischen SMPL-Modell registriert, um es kontrollierbar zu machen. Auf diese Weise wird das Beste aus beiden Welten genutzt. Wir untersuchen den Prozess der Registrierung eines parametrischen Modells, wie z. B. SMPL, auf ein 3D-Netz. Dieses jahrzehntealte Problem im Bereich der Computer Vision und der Graphik erfordert in der Regel einen zweistufigen Prozess: i) Herstellung von Korrespondenzen zwischen dem Modell und dem Netz, und ii) Optimierung des Modells, um den Abstand zwischen den entsprechenden Punkten zu minimieren. Dieser zweistufige Prozess ist nicht durchgängig differenzierbar. Wir schlagen LoopReg vor, das eine neue, auf impliziten Funktionen basierende Darstellung des Modells verwendet und die Registrierung differenzierbar macht. Semi-überwachtes LoopReg übertrifft aktuelle überwachte Methoden mit ∼100x weniger überwachten Daten. Die Modellierung des menschlichen Aussehens ist notwendig, aber nicht ausreichend, um realistische digitale Menschen zu schaffen. Wir müssen nicht nur modellieren, wie Menschen aussehen, sondern auch, wie sie mit ihren umgebenden Objekten interagieren. Zu diesem Zweck präsentieren wir mit BEHAVE den ersten Datensatz von realen Ganzkörper-Interaktionen zwischen Menschen und beweglichen Objekten. Wir stellen segmentierte Multiview-RGBDFrames zusammen mit registrierten SMPL- und Objekt-Fits sowie Kontaktannotationen in 3D zur Verfügung. Der BEHAVE-Datensatz enthält ∼15k Frames und seine Erweiterung enthält ∼400k Frames mit Pseudo-Ground-Truth-Annotationen. Unsere BEHAVE-Methode verwendet diesen Datensatz, um ein neuronales Netz zu trainieren, das die Person, das Objekt und die Kontakte zwischen ihnen gemeinsam verfolgt. In dieser Arbeit untersuchen wir die oben genannten Ideen und bieten eine eingehende Analyse unserer Schlüsselideen und Designentscheidungen. Wir erörtern auch die Grenzen unserer Ideen und schlagen künftige Arbeiten vor, um nicht nur diese Grenzen anzugehen, sondern auch die Forschung weiter auszubauen. Unser gesamter Code, die digitale Garderobe und der Datensatz sind für weitere Forschungen öffentlich zugänglich

Study on 3D modeling and pattern-making for upper garment（上衣の三次元モデルの構築およびパターンメーキングに関する研究）

信州大学(Shinshu university)博士（工学）ThesisZHANG JUN. Study on 3D modeling and pattern-making for upper garment（上衣の三次元モデルの構築およびパターンメーキングに関する研究）. 信州大学, 2017, 博士論文. 博士（工学）, 甲第663号, 平成29年03月20日授与.doctoral thesi

An investigation on the framework of dressing virtual humans

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Realistic human models are widely used in variety of applications. Much research has been carried out on improving realism of virtual humans from various aspects, such as body shapes, hair, and facial expressions and so on. In most occasions, these virtual humans need to wear garments. However, it is time-consuming and tedious to dress a human model using current software packages [Maya2004]. Several methods for dressing virtual humans have been proposed recently [Bourguignon2001, Turquin2004, Turquin2007 and Wang2003B]. The method proposed by Bourguignon et al [Bourguignon2001] can only generate 3D garment contour instead of 3D surface. The method presented by Turquin et al. [Turquin2004, Turquin2007] could generate various kinds of garments from sketches but their garments followed the shape of the body and the side of a garment looked not convincing because of using simple linear interpolation. The method proposed by Wang et al. [Wang2003B] lacked interactivity from users, so users had very limited control on the garment shape.This thesis proposes a framework for dressing virtual humans to obtain convincing dressing results, which overcomes problems existing in previous papers mentioned above by using nonlinear interpolation, level set-based shape modification, feature constraints and so on. Human models used in this thesis are reconstructed from real human body data obtained using a body scanning system. Semantic information is then extracted from human models to assist in generation of 3 dimensional (3D) garments. The proposed framework allows users to dress virtual humans using garment patterns and sketches. The proposed dressing method is based on semantic virtual humans. A semantic human model is a human body with semantic information represented by certain of structure and body features. The semantic human body is reconstructed from body scanned data from a real human body. After segmenting the human model into six parts some key features are extracted. These key features are used as constraints for garment construction.Simple 3D garment patterns are generated using the techniques of sweep and offset. To dress a virtual human, users just choose a garment pattern, which is put on the human body at the default position with a default size automatically. Users are allowed to change simple parameters to specify some sizes of a garment by sketching the desired position on the human body.To enable users to dress virtual humans by their own design styles in an intuitive way, this thesis proposes an approach for garment generation from user-drawn sketches. Users can directly draw sketches around reconstructed human bodies and then generates 3D garments based on user-drawn strokes. Some techniques for generating 3D garments and dressing virtual humans are proposed. The specific focus of the research lies in generation of 3D geometric garments, garment shape modification, local shape modification, garment surface processing and decoration creation. A sketch-based interface has been developed allowing users to draw garment contour representing the front-view shape of a garment, and the system can generate a 3D geometric garment surface accordingly. To improve realism of a garment surface, this thesis presents three methods as follows. Firstly, the procedure of garment vertices generation takes key body features as constraints. Secondly, an optimisation algorithm is carried out after generation of garment vertices to optimise positions of garment vertices. Finally, some mesh processing schemes are applied to further process the garment surface. Then, an elaborate 3D geometric garment surface can be obtained through this series of processing. Finally, this thesis proposes some modification and editing methods. The user-drawn sketches are processed into spline curves, which allow users to modify the existing garment shape by dragging the control points into desired positions. This makes it easy for users to obtain a more satisfactory garment shape compared with the existing one. Three decoration tools including a 3D pen, a brush and an embroidery tool, are provided letting users decorate the garment surface by adding some small 3D details such as brand names, symbols and so on. The prototype of the framework is developed using Microsoft Visual Studio C++,OpenGL and GPU programming

Learning to Dress {3D} People in Generative Clothing

Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shapes. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term in SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses. The model, code and data are available for research purposes at https://cape.is.tue.mpg.de.Comment: CVPR-2020 camera ready. Code and data are available at https://cape.is.tue.mpg.d

Three-dimensional garment-size change modeled considering vertical proportions

ArticleINTERNATIONAL JOURNAL OF CLOTHING SCIENCE AND TECHNOLOGY. 29(1):84-95 (2017)journal articl