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

• URL: http://arxiv.org/abs/2407.08484v1
• Date: Thu, 11 Jul 2024 13:16:02 GMT
• Title: Learning Localization of Body and Finger Animation Skeleton Joints on Three-Dimensional Models of Human Bodies
• Authors: Stefan Novaković, Vladimir Risojević,
• Abstract summary: Our work proposes one such solution to the problem of positioning body and finger animation skeleton joints within 3D models of human bodies. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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