The capability to generate simulation-ready garment models from 3D shapes of clothed humans will significantly enhance the interpretability of captured geometry of real garments, as well as their faithful reproduction in the virtual world. This will have notable impact on fields like shape capture in social VR, and virtual try-on in the fashion industry. To align with the garment modeling process standardized by the fashion industry as well as cloth simulation softwares, it is required to recover 2D patterns. This involves an inverse garment design problem, which is the focus of our work here: Starting with an arbitrary target garment geometry, our system estimates an animatable garment model by automatically adjusting its corresponding 2D template pattern, along with the material parameters of the physics-based simulation (PBS). Built upon a differentiable cloth simulator, the optimization process is directed towards minimizing the deviation of the simulated garment shape from the target geometry. Moreover, our produced patterns meet manufacturing requirements such as left-to-right-symmetry, making them suited for reverse garment fabrication. We validate our approach on examples of different garment types, and show that our method faithfully reproduces both the draped garment shape and the sewing pattern.

翻译：从着装人体的三维形状生成可直接用于模拟的服装模型，将显著提升真实服装捕获几何的可解释性及其在虚拟世界中的高保真复现能力。这一技术将对社交虚拟现实中的形体捕捉、时尚产业的虚拟试衣等领域产生重要影响。为符合时尚产业及布料模拟软件标准化的服装建模流程，需恢复二维裁片图案。这涉及逆向服装设计问题，也是本工作的核心：系统以任意目标服装几何为起点，通过自动调整其对应的二维模板图案及基于物理模拟（PBS）的材料参数，估算出可动态驱动的服装模型。基于可微分布料模拟器构建的优化过程，旨在最小化模拟服装形态与目标几何之间的偏差。此外，我们生成的图案满足左右对称等生产制造要求，适用于逆向服装制造。我们通过多种服装类型的案例验证了该方法，结果表明本方法能够精确复现悬垂服装形态与缝制图案。