Pan, X, Huang, J, Mai, J et al. (5 more authors) (2021) HeterSkinNet: A Heterogeneous Network for Skin Weights Prediction. In: Proceedings of the ACM on Computer Graphics and Interactive Techniques. I3D 21 Symposium on Interactive 3D Graphics and Games, 05-07 May 2021, Online. Association for Computing Machinery
Abstract
Character rigging is universally needed in computer graphics but notoriously laborious. We present a new method, HeterSkinNet, aiming to fully automate such processes and significantly boost productivity. Given a character mesh and skeleton as input, our method builds a heterogeneous graph that treats the mesh vertices and the skeletal bones as nodes of different types and uses graph convolutions to learn their relationships. To tackle the graph heterogeneity, we propose a new graph network convolution operator that transfers information between heterogeneous nodes. The convolution is based on a new distance HollowDist that quantifies the relations between mesh vertices and bones. We show that HeterSkinNet is robust for production characters by providing the ability to incorporate meshes and skeletons with arbitrary topologies and morphologies (e.g., out-of-body bones, disconnected mesh components, etc.). Through exhaustive comparisons, we show that HeterSkinNet outperforms state-of-the-art methods by large margins in terms of rigging accuracy and naturalness. HeterSkinNet provides a solution for effective and robust character rigging.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 ACM. This is an author produced version of a conference paper published in Proceedings of the ACM on Computer Graphics and Interactive Techniques. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Mar 2021 09:42 |
Last Modified: | 08 May 2021 00:25 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/3451262 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171781 |