论文标题
Rignet：铰接字符的神经索具
RigNet: Neural Rigging for Articulated Characters
论文作者
论文摘要
我们提出Rignet，这是一种用于从输入字符模型生成动画钻机的端到端自动化方法。给定一个代表铰接性角色的输入3D模型，Rignet预测了与动画师期望在关节放置和拓扑中相匹配的骨骼。它还根据预测的骨骼估算表面皮肤重量。我们的方法基于一个直接在网格表示上运行的深度体系结构，而无需对形状类别和结构做出假设。该体系结构经过了大量且多样化的型号，包括其网状，骨骼和相应的皮肤重量。我们的评估是三倍：与动画钻机相比，我们比以前的艺术表现出更好的结果；从定性上讲，我们表明我们的钻机可以在多个细节上表达和动画。最后，我们评估了各种算法选择对输出钻机的影响。
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.
