DenseGATs: A Graph-Attention-Based Network for Nonlinear Character Deformation
DenseGATs: A Graph-Attention-Based Network for Nonlinear Character Deformation
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DOI：
10.1145/3384382.3384525
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发表时间：
2020-05
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影响因子：
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通讯作者：
Tianxing Li;Rui Shi;T. Kanai
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作者：
Tianxing Li;Rui Shi;T. Kanai
在动画制作中，动画师总是花费大量的时间和精力来为具有复杂外观和细节的角色开发高质量的变形系统。为了减少重复蒙皮和微调工作所花费的时间，我们提出了一种端到端的方法，根据高质量蒙皮角色网格的现有图形信息自动计算新角色的变形。我们采用将网格变形视为线性和非线性部分的组合的思想，并提出了一种近似复杂非线性变形的新颖架构。另一方面，线性变形很简单，因此可以直接计算，尽管不精确。为了使我们的网络能够处理复杂的图数据并归纳预测非线性变形，我们设计了基于图注意（GAT）的块，由聚合流和自增强流组成，以聚合相邻节点的特征并增强单个图节点的特征。为了降低学习大量网格特征的难度，我们在一组 GAT 块之间引入了一种称为“密集模块”的密集连接模式，以确保特征在我们的深层框架中的传播。这些策略允许将现有的皮肤良好的角色模型的变形特征与新模型共享，我们将其称为密集连接图注意网络（DenseGAT）。我们测试了 DenseGAT，并将其与经典变形方法和其他基于图学习的策略进行了比较。实验证实，我们的网络可以预测看不见的角色的高度合理的变形。
In animation production, animators always spend significant time and efforts to develop quality deformation systems for characters with complex appearances and details. In order to decrease the time spent repetitively skinning and fine-tuning work, we propose an end-to-end approach to automatically compute deformations for new characters based on existing graph information of high-quality skinned character meshes. We adopt the idea of regarding mesh deformations as a combination of linear and nonlinear parts and propose a novel architecture for approximating complex nonlinear deformations. Linear deformations on the other hand are simple and therefore can be directly computed, although not precisely. To enable our network handle complicated graph data and inductively predict nonlinear deformations, we design the graph-attention-based (GAT) block to consist of an aggregation stream and a self-reinforced stream in order to aggregate the features of the neighboring nodes and strengthen the features of a single graph node. To reduce the difficulty of learning huge amount of mesh features, we introduce a dense connection pattern between a set of GAT blocks called “dense module” to ensure the propagation of features in our deep frameworks. These strategies allow the sharing of deformation features of existing well-skinned character models with new ones, which we call densely connected graph attention network (DenseGATs). We tested our DenseGATs and compared it with classical deformation methods and other graph-learning-based strategies. Experiments confirm that our network can predict highly plausible deformations for unseen characters.