This paper proposes an anchor-based deformation model, namely AnchorDEF, to
predict 3D garment animation from a body motion sequence. It deforms a garment
mesh template by a mixture of rigid transformations with extra nonlinear
displacements. A set of anchors around the mesh surface is introduced to guide
the learning of rigid transformation matrices. Once the anchor transformations
are found, per-vertex nonlinear displacements of the garment template can be
regressed in a canonical space, which reduces the complexity of deformation
space learning. By explicitly constraining the transformed anchors to satisfy
the consistencies of position, normal and direction, the physical meaning of
learned anchor transformations in space is guaranteed for better
generalization. Furthermore, an adaptive anchor updating is proposed to
optimize the anchor position by being aware of local mesh topology for learning
representative anchor transformations. Qualitative and quantitative experiments
on different types of garments demonstrate that AnchorDEF achieves the
state-of-the-art performance on 3D garment deformation prediction in motion,
especially for loose-fitting garments.

本文提出了一种基于锚点的变形模型 AnchorDEF，利用非线性位移和混合刚性变换来预测身体运动状态下的三维服装动画，通过锚点来指导刚性变换矩阵的学习，并采用自适应锚点更新来优化锚点位置，从而实现更好的三维服装变形预测性能。