薄壳模型的深度变形细节综合
Deep Deformation Detail Synthesis for Thin Shell Models
摘要：
在基于物理的布料动画中，丰富的褶皱和详细的褶皱是以昂贵的计算资源和巨大的劳动调整为代价的。数据驱动技术致力于通过数据库显著减少计算。一种类型的方法依赖于人的姿势来合成不能应用于普通布料的合身服装。另一种类型的方法在没有这种限制的情况下向粗网格添加细节。然而，现有的工作通常使用基于坐标的表示，不能应付大规模的变形，并要求密集的粗和细网格之间的顶点对应。此外，由于这些方法仅添加细节，因此它们需要粗网格接近细网格，这可能是不可能的，或者在生成细网格时需要不切实际的约束。为了解决这些挑战，我们开发了一个时间和空间上尽可能一致的变形表示（称为TS-ACAP）和DeformTransformer网络来学习从低分辨率网格到详细网格的映射。此TS-ACAP表示旨在确保布料动画连续大规模变形的空间和时间一致性。通过这种表示，我们的DeformTransformer网络首先使用两个基于网格的编码器来分别提取粗特征和细特征。为了将粗略特征转换为精细特征，我们利用由帧级注意力机制组成的Transformer网络来确保预测的时间一致性。实验结果表明，该方法能够在高帧速率下，在不同的数据集上生成可靠的真实感动画，比基于物理的模拟快10 ~ 35倍，并且具有比现有方法更好的细节合成能力.
英文摘要：
In physics-based cloth animation, rich folds and detailed wrinkles are achieved at the cost of expensive computational resources and huge labor tuning. Data-driven techniques make efforts to reduce the computation significantly by a database. One type of methods relies on human poses to synthesize fitted garments which cannot be applied to general cloth. Another type of methods adds details to the coarse meshes without such restrictions. However, existing works usually utilize coordinate-based representations which cannot cope with large-scale deformation, and requires dense vertex correspondences between coarse and fine meshes. Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes. To address these challenges, we develop a temporally and spatially as-consistent-as-possible deformation representation (named TS-ACAP) and a DeformTransformer network to learn the mapping from low-resolution meshes to detailed ones. This TS-ACAP representation is designed to ensure both spatial and temporal consistency for sequential large-scale deformations from cloth animations. With this representation, our DeformTransformer network first utilizes two mesh-based encoders to extract the coarse and fine features, respectively. To transduct the coarse features to the fine ones, we leverage the Transformer network that consists of frame-level attention mechanisms to ensure temporal coherence of the prediction. Experimental results show that our method is able to produce reliable and realistic animations in various datasets at high frame rates: 10 ~ 35 times faster than physics-based simulation, with superior detail synthesis abilities than existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
