HuCollisionField: Resolving Self-Collisions via Neural Fields for Human Prediction

Self-collision remains a persistent challenge in SMPL-basedhuman pose estimation and motion generation. Under extreme articula-tions or stochastic motion synthesis, generated meshes frequently exhibitself-penetrations, leading to physically implausible results. We proposePoseShield, a neural collision constraint defined directly in SMPL posespace. We formulate collision correction as a constrained optimizationproblem and connect the learned constraint with the Eikonal equation.Enforcing Eikonal regularization ensures non-vanishing gradients nearthe collision boundary, improving numerical stability and robustness ofthe optimization process. Unlike prior methods that operate in the meshspace or rely on heuristic penalties, our approach operates directly in thelow-dimensional space of human poses and is theoretically grounded. Thesame learned constraint extends to human motion sequences, providinga generator-agnostic post-hoc collision corrector without retraining theunderlying motion model. Experiments on a newly constructed SMPLpose benchmark show that our method achieves a 95.8% success rateand outperforms state-of-the-art baselines.