1,338 research outputs found
RECREATING AND SIMULATING DIGITAL COSTUMES FROM A STAGE PRODUCTION OF \u3ci\u3eMEDEA\u3c/i\u3e
This thesis investigates a technique to effectively construct and simulate costumes from a stage production Medea, in a dynamic cloth simulation application like Maya\u27s nDynamics. This was done by using data collected from real-world fabric tests and costume construction in the theatre\u27s costume studio. Fabric tests were conducted and recorded, by testing costume fabrics for drape and behavior with two collision objects. These tests were recreated digitally in Maya to derive appropriate parameters for the digital fabric, by comparing with the original reference. Basic mannequin models were created using the actors\u27 measurements and skeleton-rigged to enable animation. The costumes were then modeled and constrained according to the construction process observed in the costume studio to achieve the same style and stitch as the real costumes. Scenes selected and recorded from Medea were used as reference to animate the actors\u27 models. The costumes were assigned the parameters derived from the fabric tests to produce the simulations. Finally, the scenes were lit and rendered out to obtain the final videos which were compared to the original recordings to ascertain the accuracy of simulation. By obtaining and refining simulation parameters from simple fabric collision tests, and modeling the digital costumes following the procedures derived from real-life costume construction, realistic costume simulation was achieved
PBNS: physically based neural simulation for unsupervised garment pose space deformation
We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to un-supervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth. While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
Three dimensional simulation of cloth drape
Research has been carried out in the study of cloth modelling over many decades.
The more recent arrival of computers however has meant that the necessary
complex calculations can be performed quicker and that visual display of the
results is more realistic than for the earlier models.
Today's textile and garment designers are happy to use the latest two dimensional
design and display technology to create designs and experiment with patterns and
colours. The computer is seen as an additional tool that performs some of the
more tedious jobs such as re-drawing, re-colouring and pattern sizing.
Designers have the ability and experience to visualise their ideas without the need
for photo reality. However the real garment must be created when promoting
these ideas to potential customers. Three dimensional computer visualisation of a
garment can remove the need to create the garment until after the customer has
placed an order.
As well as reducing costs in the fashion industry, realistic three dimensional cloth
animation has benefits for the computer games and film industries.
This thesis describes the development of a realistic cloth drape model. The
system uses the Finite Element Method for the draping equations and graphics
routines to enhance the visual display. During the research the problem of
collision detection and response involving dynamic models has been tackled and a
unique collision detection method has been developed. This method has proved
very accurate in the simulation of cloth drape over a body model and is also
described in the thesis.
Three dimensional design and display are seen as the next logical steps to current
two dimensional practices in the textiles industry. This thesis outlines current and
previous cloth modelling studies carried out by other research groups. It goes on
to provide a full description of the drape method that has been developed during
this research period
ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns
Many approaches to draping individual garments on human body models are
realistic, fast, and yield outputs that are differentiable with respect to the
body shape on which they are draped. However, they are either unable to handle
multi-layered clothing, which is prevalent in everyday dress, or restricted to
bodies in T-pose. In this paper, we introduce a parametric garment
representation model that addresses these limitations. As in models used by
clothing designers, each garment consists of individual 2D panels. Their 2D
shape is defined by a Signed Distance Function and 3D shape by a 2D to 3D
mapping. The 2D parameterization enables easy detection of potential collisions
and the 3D parameterization handles complex shapes effectively. We show that
this combination is faster and yields higher quality reconstructions than
purely implicit surface representations, and makes the recovery of layered
garments from images possible thanks to its differentiability. Furthermore, it
supports rapid editing of garment shapes and texture by modifying individual 2D
panels.Comment: NeurIPS 202
Real-time simulation and visualisation of cloth using edge-based adaptive meshes
Real-time rendering and the animation of realistic virtual environments and characters
has progressed at a great pace, following advances in computer graphics hardware
in the last decade. The role of cloth simulation is becoming ever more important in
the quest to improve the realism of virtual environments.
The real-time simulation of cloth and clothing is important for many applications
such as virtual reality, crowd simulation, games and software for online clothes shopping.
A large number of polygons are necessary to depict the highly
exible nature of
cloth with wrinkling and frequent changes in its curvature. In combination with the
physical calculations which model the deformations, the effort required to simulate
cloth in detail is very computationally expensive resulting in much diffculty for its
realistic simulation at interactive frame rates. Real-time cloth simulations can lack
quality and realism compared to their offline counterparts, since coarse meshes must
often be employed for performance reasons.
The focus of this thesis is to develop techniques to allow the real-time simulation of
realistic cloth and clothing. Adaptive meshes have previously been developed to act as
a bridge between low and high polygon meshes, aiming to adaptively exploit variations
in the shape of the cloth. The mesh complexity is dynamically increased or refined to
balance quality against computational cost during a simulation. A limitation of many
approaches is they do not often consider the decimation or coarsening of previously
refined areas, or otherwise are not fast enough for real-time applications.
A novel edge-based adaptive mesh is developed for the fast incremental refinement
and coarsening of a triangular mesh. A mass-spring network is integrated into
the mesh permitting the real-time adaptive simulation of cloth, and techniques are
developed for the simulation of clothing on an animated character
A spring force formulation for elastically deformable models
Cataloged from PDF version of article.Continuous deformable models are generally represented using a grid of control points. The elastic properties are then modeled using the interactions between these points. The formulations based on elasticity theory express these interactions using stiffness matrices. These matrices store the elastic properties of the models and they should be evolved in time according to changing elastic properties of the models. However, forming the stiffness matrices at any step of an animation is very difficult and sometimes the differential equations that should be solved to produce animation become ill-conditioned. Instead of modeling the elasticities using stiffness matrices, the interactions between model points could be expressed in terms of external spring forces. In this paper, a spring force formulation for animating elastically deformable models is presented. In this formulation, elastic properties of the materials are represented as external spring forces as opposed to forming complicated stiffness matrices. (C) 1997 Elsevier Science Ltd
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