We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. The architecture is trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. Our evaluation is three-fold: we show better results than prior art when quantitatively compared to animator rigs; qualitatively we show that our rigs can be expressively posed and animated at multiple levels of detail; and finally, we evaluate the impact of various algorithm choices on our output rigs.

翻译：我们介绍RigNet, 这是一种从输入字符模型中生成动画机的端到端自动方法。 3D 输入模型代表了一个清晰的字符, RigNet 预测了一个与联合安置和地形学中的动画师期望相符的骨骼。 它还根据预测的骨骼估计表面皮肤重量。 我们的方法基于一个在网状图示上直接运行的深层结构,而没有对形状等级和结构作出假设。 建筑在大量多样的操纵模型(包括网目、骨骼和相应的皮肤重量)上进行了培训。 我们的评估有三重:与模拟器设备相比,我们比以往的艺术显示出更好的效果; 从质量上看,我们显示我们的钻机可以以多种详细程度表达和动画; 最后,我们评估了各种算法选择对输出机的影响。