.png){kind=icon}
返回
Robust Biharmonic Skinning Using Geometric Fields
.png){kind=icon}
PRE
AI
DOI：10.1145/3771928.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Bounded bihramonic weights are a popular tool used to rig and deform characters for animation, to compute reduced-order simulations, and to define feature descriptors for geometry processing. They necessitate tetrahedralizing the volume bounded by the surface, introducing the possibility of meshing artifacts or tetrahedralization failure. We introduce a mesh-free and robust automatic skinning technique that generates weights comparable to the current state of the art, but works reliably even on open surfaces, triangle soups, and point clouds where current methods fail. We achieve this through the use of a specialized Lagrangian representation enabled by the advent of hardware ray-tracing, which circumvents the need for finite elements while optimizing the biharmonic energy and enforcing boundary conditions. The flexibility of our formulation allows us to integrate artistic control through weight painting during the optimization. We offer a thorough qualitative and quantitative evaluation of our method.
Keyword:
Skinning weights
bounded biharmonic weights
geometry processing
deformation
partial differential equations
variational problems
期刊
IF:
9.5
论文数:
4.7K
被引数:
3.6W
机构
引用论文
.png){kind=icon}
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架
OAAI
OAAI分享收藏Smoothed particle hydrodynamics: theory and application to non-spherical stars光滑粒子流体动力学: 非球形恒星的理论和应用
PREAI
分享收藏Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks在物理通知的深度神经网络中使用距离函数精确施加边界条件OAAI
OAAI分享收藏
查看更多内容