Multi-scale edge aggregation mesh-graph-network for character secondary motion

As an enhancement to skinning-based animations, light-weight secondary motion method for 3D characters are widely demanded in many application scenarios. To address the dependence of data-driven methods on ground truth data, we propose a self-supervised training strategy that is free of ground truth data for the first time in this domain. Specifically, we construct a self-supervised training framework by modeling the implicit integration problem with steps as an optimization problem based on physical energy terms. Furthermore, we introduce a multi-scale edge aggregation mesh-graph block (MSEA-MG Block), which significantly enhances the network performance. This enables our model to make vivid predictions of secondary motion for 3D characters with arbitrary structures. Empirical experiments indicate that our method, without requiring ground truth data for model training, achieves comparable or even superior performance quantitatively and qualitatively compared to state-of-the-art data-driven approaches in the field.