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arXiv 2407.08484cs.CV
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在人体三维模型上学习身体与手指动画骨骼关节的定位

Learning Localization of Body and Finger Animation Skeleton Joints on Three-Dimensional Models of Human Bodies

Faculty of Electrical Engineering University of Banja Luka Banja Luka, Bosnia

Stefan Novaković, Vladimir Risojević

AI总结:

针对人体3D模型中身体与手指动画骨骼关节定位问题,提出基于动态图卷积神经网络的方法,用合成数据训练,以输入点凸组合计算关节位置,架构更简单、手指关节定位效果更优且处理速度更快。

摘要:

当前解决各类需分析三维(3D)网格与点云的问题的方法,已采用直接处理点坐标、法向量、顶点连接信息等3D数据的深度学习算法。我们的工作提出了一种在人体3D模型中定位身体与手指动画骨骼关节的解决方案。由于带标注的真实人体扫描数据稀缺,我们通过改变形状与姿态参数生成合成样本。与现有最优方法类似,我们的方法将每个关节位置计算为输入点的凸组合。仅以点坐标列表与法向量估计值作为输入,使用动态图卷积神经网络预测凸组合的系数。通过将我们的方法与现有最优方法对比,我们证明使用更简单的架构就能取得显著更优的结果,尤其是在手指关节方面。由于我们的方案所需的预计算特征更少,处理时间也更短。

英文摘要:

Contemporary approaches to solving various problems that require analyzing three-dimensional (3D) meshes and point clouds have adopted the use of deep learning algorithms that directly process 3D data such as point coordinates, normal vectors and vertex connectivity information. Our work proposes one such solution to the problem of positioning body and finger animation skeleton joints within 3D models of human bodies. Due to scarcity of annotated real human scans, we resort to generating synthetic samples while varying their shape and pose parameters. Similarly to the state-of-the-art approach, our method computes each joint location as a convex combination of input points. Given only a list of point coordinates and normal vector estimates as input, a dynamic graph convolutional neural network is used to predict the coefficients of the convex combinations. By comparing our method with the state-of-the-art, we show that it is possible to achieve significantly better results with a simpler architecture, especially for finger joints. Since our solution requires fewer precomputed features, it also allows for shorter processing times.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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