提出新型3D动力学建模方法，能高效捕捉未观测的多体相互作用。
PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling
- 基于能量函数轨迹最小化设计物理启发注意力机制
- 在人体动作、分子动力学等任务上误差降低4.7%至41.5%
- 适合需要高精度物理模拟的科研与工程场景
3D动力学建模是多体系统中基础性问题，在物体轨迹预测与仿真中具有重要应用。尽管近期基于图神经网络的方法通过引入几何对称性、高阶特征编码或神经微分方程力学机制取得了良好表现，但通常依赖显式可观测结构，难以捕捉复杂物理行为中至关重要的未观测相互作用。本文提出PAINET，一种基于SE(3)等变性的新型变压器，用于学习多体系统中的全连接相互作用。模型包含：（1）源自能量函数轨迹最小化的物理启发注意力网络；（2）保持等变性的同时实现高效推理的并行解码器。在真实世界多种基准测试中，包括人体动作捕捉、分子动力学和大规模蛋白质模拟，实验结果表明PAINET持续优于现有模型，在3D动态预测中实现4.7%至41.5%的误差降低，计算时间与内存消耗相当。
原文摘要 · Abstract (English)
Modeling 3D dynamics is a fundamental problem in multi-body systems across scientific and engineering domains and has important practical implications in object trajectory prediction and simulation. While recent GNN-based approaches have achieved strong performance by enforcing geometric symmetries, encoding high-order features or incorporating neural-ODE mechanics, they typically depend on explicitly observed structures and inherently fail to capture the unobserved interactions that are crucial to complex physical behaviors and dynamics mechanism. In this paper, we propose PAINET, a principled SE(3)-equivariant transformer for learning all-pair interactions in multi-body systems. The model comprises: (1) a novel physics-inspired attention network derived from the minimization trajectory of an energy function, and (2) a parallel decoder that preserves equivariance while enabling efficient inference. Empirical results on diverse real-world benchmarks, including human motion capture, molecular dynamics, and large-scale protein simulations, show that PAINET consistently outperforms recently proposed models, yielding 4.7% to 41.5% error reductions in 3D dynamics prediction with comparable computation costs in terms of time and memory. Our codes, baseline models and datasets are available at https://github.com/Icarus1411/PAINET.
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