论文标题
DeepSD：3D服装动画的自动深层皮肤和姿势空间变形
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation
论文作者
论文摘要
我们通过深度学习为服装动画问题提供了一种新颖的解决方案。我们的贡献允许使用任意拓扑和几何复杂性来为任何模板服装动画。最近的作品通过利用支撑车身模型（编码服装作为身体同型）来开发服装版本，调整和动画的模型。这导致了复杂的工程解决方案，这些解决方案遭受了可扩展性，适用性和兼容性的影响。通过将我们的范围限制在服装动画上，我们能够提出一个简单的模型，该模型可以独立于其拓扑，顶点订单或连接性来使任何服装动画。我们提出的体系结构将动画3D模型映射到3D动画的标准格式（混合重量和混合形状矩阵），自动提供与任何图形引擎的兼容性。我们还提出了一种方法，可以通过无监督的基于身体的学习来补充监督的学习，该学习隐含地解决碰撞并增强了布质的质量。
We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.
