PoseShield：用於人體自碰撞解析的神經碰撞場

摘要

在基於SMPL的人體姿態估計與動作生成中，自碰撞仍舊是一項持續存在的挑戰。在極端關節角度或隨機動作合成下，生成的網格時常出現自穿透現象，導致物理上不合理的結果。我們提出PoseShield，這是一種直接在SMPL姿態空間中定義的神經碰撞約束。我們將碰撞修正表述為一個約束優化問題，並將學習到的約束與程函方程（Eikonal equation）相連結。施加程函正則化確保碰撞邊界附近梯度不為零，從而提升優化過程的數值穩定性與強健性。與先前在網格空間中操作或依賴啟發式懲罰項的方法不同，我們的方法直接在低維度的人體姿態空間中運作，且具備理論基礎。該學習到的約束可進一步擴展至人體動作序列，成為一個無需重新訓練底層動作模型的、與生成器無關的事後碰撞修正器。在全新建構的SMPL姿態基準測試上的實驗顯示，我們的方法達到了95.8%的成功率，並優於現有的最佳基準方法。

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.