RigNet: Neural Rigging for Articulated Characters
RigNet: Neural Rigging for Articulated Characters
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DOI：
10.1145/3386569.3392379
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发表时间：
2020-07-01
影响因子：
6.2
通讯作者：
Singh, Karan
中科院分区：
文献类型：
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作者：
Xu, Zhan;Zhou, Yang;Singh, Karan
我们提出了RigNet，一个端到端的自动化方法，用于从输入角色模型中生成动画钻机。给定一个代表关节角色的输入3D模型，RigNet预测出一个与动画师在关节位置和拓扑结构方面的期望相匹配的骨架。它还根据预测的骨架来估计表面皮肤的重量。我们的方法是基于一种深度架构，它直接对网格表示进行操作，而不需要对形状类和结构进行假设。该架构是在大量不同的装配模型上进行训练的，包括它们的网格、骨架和相应的皮肤权重。我们的评估有三个方面：当与动画设备进行定量比较时，我们呈现出比现有技术更好的结果；定性地说，我们表明我们的钻机可以在多个细节层次上表达和动画；最后，我们评估了各种算法选择对输出设备的影响。（1）
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.(1)