DeepDraper: Fast and Accurate 3D Garment Draping over a 3D Human Body

Lokender Tiwari, Brojeshwar Bhowmick

Abstract:

Draping a 3D human mesh has garnered broad interest due to its wide applicability in virtual try-on, animations, etc. The 3D garment deformations produced by the existing methods are often inconsistent with the body shape, pose, and measurements. This paper proposes a single unified learning-based framework (DeepDraper) to predict gar- ment deformation as a function of body shape, pose, mea- surements, and garment styles. We train the DeepDraper with coupled geometric and multi-view perceptual losses. Unlike existing methods, we additionally model garment de- formations as a function of standard body measurements, which generally a buyer or a designer uses to buy or de- sign perfect fit clothes. As a result, DeepDraper signifi- cantly outperforms the state-of-the-art deep network-based approaches in terms of fitness and realism and generalizes well to the unseen style of the garments. In addition to that, DeepDraper is ∼ 10 times smaller in size and ∼ 23 times faster than the closest state-of-the-art method (Tailor- Net), which favors its use in real-time applications with less computational power. Despite being trained on the static poses of the TailorNet [32] dataset, DeepDraper general-
izes well to unseen body shapes, poses, and garment styles and produces temporally coherent garment deformations on the pose sequences even from the unseen AMASS [25] dataset.