Motion tracking has been an important technique for imitating human-like movement from large-scale datasets in physics-based motion synthesis. However, existing approaches focus on tracking either single character or a particular type of interaction, limiting their ability to handle contact-rich interactions. Extending single-character tracking approaches suffers from the instability due to the challenge of forces transferred through contacts. Contact-rich interactions requires levels of control, which places much greater demands on model capacity. To this end, we propose a robust tracking method based on progressive neural network (PNN) where multiple experts are specialized in learning skills of various difficulties. Our method learns to assign training samples to experts automatically without requiring manually scheduling. Both qualitative and quantitative results show that our method delivers more stable motion tracking in densely interactive movements while enabling more efficient model training.

翻译：运动追踪是从大规模数据集中模仿类人运动的重要技术，在基于物理的运动合成领域具有关键作用。然而，现有方法主要聚焦于单一角色追踪或特定交互类型，难以处理密集接触类交互。直接扩展单一角色追踪方法会因接触力传递带来的不稳定问题而导致性能下降。密集接触交互需要多层级控制能力，这对模型容量提出了更高要求。为此，我们提出了一种基于渐进式神经网络（PNN）的鲁棒追踪方法，该方法包含多个专家模块，专门学习不同难度的技能。该方法无需人工调度即可自动将训练样本分配给相应专家。定性与定量结果均表明，本方法在密集交互运动中能实现更稳定的运动追踪，同时提升模型训练效率。