Paper page - PoseShield: Neural Collision Fields for Human Self-Collision Resolution

Source: https://huggingface.co/papers/2606.29686

Abstract

PoseShield addresses self-collision issues in SMPL-based human pose estimation by applying neural collision constraints in pose space through constrained optimization and Eikonal regularization.

Self-collisionremains a persistent challenge inSMPL-based humanpose estimationandmotion generation. Under extreme articulations or stochastic motion synthesis, generated meshes frequently exhibit self-penetrations, leading to physically implausible results. We propose PoseShield, aneural collision constraintdefined directly inSMPLpose space. We formulate collision correction as aconstrained optimizationproblem and connect the learned constraint with theEikonal 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 constructedSMPLpose benchmark show that our method achieves a 95.8% success rate and outperforms state-of-the-art baselines.

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