While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a
3D body, it remains too costly for real-time applications, such as virtual
try-on. By contrast, inference in a deep network, requiring a single forward
pass, is much faster. Taking advantage of this, we propose a novel architecture
to fit a 3D garment template to a 3D body. Specifically, we build upon the
recent progress in 3D point cloud processing with deep networks to extract
garment features at varying levels of detail, including point-wise, patch-wise
and global features. We fuse these features with those extracted in parallel
from the 3D body, so as to model the cloth-body interactions. The resulting
two-stream architecture, which we call as GarNet, is trained using a loss
function inspired by physics-based modeling, and delivers visually plausible
garment shapes whose 3D points are, on average, less than 1 cm away from those
of a PBS method, while running 100 times faster. Moreover, the proposed method
can model various garment types with different cutting patterns when parameters
of those patterns are given as input to the network.

该论文提出了一种名为 GarNet 的两流体系结构，利用深度网络从三维点云中提取不同级别的服装特征，并将其与从三维物体并行提取的特征融合，以建模衣物 - 身体交互，最终可在 100 倍速下生成视觉上合理的服装形状，同时平均三维点距离少于 1 厘米，可用于不同类型衣服的建模。