HeterSkinNet: a heterogeneous network for skin weights prediction
HeterSkinNet: a heterogeneous network for skin weights prediction
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HeterSkinNet：用于皮肤权重预测的异构网络
DOI：
10.1145/3451262
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发表时间：
2021
影响因子：
1.3
通讯作者：
XIAOGANG JIN
中科院分区：
文献类型：
--
作者：
XIAOYU PAN;JIANCONG HUANG;JIAMING MAI;HE WANG;HONGLIN LI;TONGKUI SU;WENJUN WANG;XIAOGANG JIN
字符操纵是计算机图形学中普遍需要的，但众所周知是费力的。我们提出了一种新的方法，HeterSkinNet，旨在完全自动化这些过程，并显着提高生产力。给定一个角色网格和骨架作为输入，我们的方法构建了一个异构图，将网格顶点和骨架骨骼视为不同类型的节点，并使用图卷积来学习它们之间的关系。为了解决图的异构性，我们提出了一种新的图网络卷积算子，在异构节点之间传输信息。卷积是基于一个新的距离HollowDist，量化网格顶点和骨骼之间的关系。我们表明，HeterSkinNet通过提供将网格和骨架与任意拓扑和形态（例如，体外骨骼、断开的网格组件等）。通过详尽的比较，我们表明，HeterSkinNet在装配精度和自然度方面远远优于最先进的方法。HeterSkinNet提供了一个有效和强大的角色操纵解决方案。
Character rigging is universally needed in computer graphics but notoriously laborious. We present a new method, HeterSkinNet, aiming to fully automate such processes and significantly boost productivity. Given a character mesh and skeleton as input, our method builds a heterogeneous graph that treats the mesh vertices and the skeletal bones as nodes of different types and uses graph convolutions to learn their relationships. To tackle the graph heterogeneity, we propose a new graph network convolution operator that transfers information between heterogeneous nodes. The convolution is based on a new distance HollowDist that quantifies the relations between mesh vertices and bones. We show that HeterSkinNet is robust for production characters by providing the ability to incorporate meshes and skeletons with arbitrary topologies and morphologies (e.g., out-of-body bones, disconnected mesh components, etc.). Through exhaustive comparisons, we show that HeterSkinNet outperforms state-of-the-art methods by large margins in terms of rigging accuracy and naturalness. HeterSkinNet provides a solution for effective and robust character rigging.