This paper presents an interactive technique that
produces static hairstyles by generating individual hair strands
of the desired shape and color, subject to the presence of gravity
and collisions. A variety of hairstyles can be generated by
adjusting the wisp parameters, while the deformation is solved
efficiently, accounting for the effects of gravity and collisions.
Wisps are generated employing statistical approaches. As for
hair deformation, we propose a method which is based on
physical simulation concepts but is simplified to efficiently
solve the static shape of hair. On top of the statistical wisp
model and the deformation solver, a constraint-based styler
is proposed to model artificial features that oppose the natural
flow of hair under gravity and hair elasticity, such as a hairpin.
Our technique spans a wider range of human hairstyles than
previously proposed methods, and the styles generated by this
technique are fairly realistic.
標簽：
interactive
hairstyles
generating
technique
上傳時間：
2013-12-20
上傳用戶：sssl
Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
標簽：
Optimization and Uncertainty Quantification
上傳時間：
2016-04-08
上傳用戶：huhu123456
