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 unsupervisedly 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.
翻译:我们提出一种方法,通过深层学习,自动获得装饰服装装饰的Pose Sace Deformat(PSD)基础。古典方法依赖于基于物理的模拟(PBS)和动画衣。这些是一般的解决方案,由于空间和时间的细微分解,因此可以取得非常现实的结果。然而,这些解决方案在计算上成本昂贵,任何场景的修改都促使需要重新模拟。由私营部门司提供的线性皮肤(LBS)为PBS提供了一种轻巧的替代品。虽然它需要大量的数据来学习适当的私营部门。我们建议使用深度的学习,作为隐含的 PBS 模的兼容性。我们建议使用不超前的深度学习,以隐含的 PBS 格式来学习现实的布局空间变异性布局的布局。此外,我们经常提议用复杂的方法来训练这些模型,与几部的PBS相仿造相仿。我们最了解的是,我们首先可以提出一个神经的模拟变质模拟的布。虽然任何深基方法正在演变一种不透明的趋势,但是,但是, 它们是不透明的模型是不固定的模型是不精确的模型, 。此外的模型会提出一种不耐动的模型。此外的模型。此外的模型,我们常常会显示我们用的方法需要更精确的模型会显示我们用的方法需要更精确的。