用神经碰撞场解决人体姿态自穿透问题，提升动作生成真实性
PoseShield: Neural Collision Fields for Human Self-Collision Resolution
- 在SMPL姿态空间直接建模碰撞约束，避免网格空间计算复杂性
- 95.8%成功率解决极端姿态下的自穿透，优于现有最先进方法
- 无需重训练模型，可通用修复各类运动生成结果
自碰撞仍是基于SMPL的人体姿态估计与动作生成中的长期挑战。在极端关节姿态或随机运动合成中，生成的网格常出现自穿透，导致物理上不合理的结果。我们提出PoseShield，一种直接定义在SMPL姿态空间的神经碰撞约束。将碰撞修正建模为带约束的优化问题，并将学习到的约束与Eikonal方程关联。施加Eikonal正则化可确保碰撞边界附近梯度不消失，提升优化过程的数值稳定性和鲁棒性。与以往在网格空间操作或依赖启发式惩罚的方法不同，本方法直接在低维人体姿态空间运行，具有理论基础。相同学习约束可扩展至人体动作序列，作为无需重训练的通用后处理矫正器。在新构建的SMPL姿态基准测试中，该方法达到95.8%的成功率，显著优于现有最先进基线。
原文摘要 · Abstract (English)
Self-collision remains a persistent challenge in SMPL-based human pose estimation and motion generation. Under extreme articulations or stochastic motion synthesis, generated meshes frequently exhibit self-penetrations, leading to physically implausible results. We propose PoseShield, a neural collision constraint defined directly in SMPL pose space. We formulate collision correction as a constrained optimization problem and connect the learned constraint with the Eikonal equation. Enforcing Eikonal regularization ensures non-vanishing gradients near the collision boundary, improving numerical stability and robustness of the optimization process. Unlike prior methods that operate in the mesh space or rely on heuristic penalties, our approach operates directly in the low-dimensional space of human poses and is theoretically grounded. The same learned constraint extends to human motion sequences, providing a generator-agnostic post-hoc collision corrector without retraining the underlying motion model. Experiments on a newly constructed SMPL pose benchmark show that our method achieves a 95.8% success rate and outperforms state-of-the-art baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。