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.
翻译:计算机图形中普遍需要字符固定,但非常困难。 我们展示了一种新的方法, HeterSkinNet, 旨在完全自动化这些流程并大幅提高生产率。 基于一个字符网和骨骼作为输入, 我们的方法构建了一个将网状脊椎和骨骨作为不同类型节点的多元图解图, 并使用图形组合来了解它们的关系。 为了解决图形差异性, 我们提议一个新的图形网络连接操作器, 在不同节点之间传输信息。 共进基于一个新的距离的Hollow Dist, 来量化网状面和骨骼之间的关系。 我们显示 HeterSkinNet对生产字符来说是强大的, 提供了将网状和骨骼与任意的表层和形态( 例如, 体外骨、 断开的网形元素等) 的整合能力。 通过详尽的比较, 我们显示 HeterSkinNet 网络在精确性和自然性能方面有很大的边缘, 提供了一种有效的解决方案。