Content moderation in online multiplayer 3D virtual environments is increasingly automated, yet detection has focused on images, video, and audio, leaving suggestive motion a blind spot. We present a motion-only classification pipeline that detects suggestive and explicit movement from SMPL skeleton trajectories using Laban Movement Analysis (LMA) descriptors. On a dataset spanning everyday, artistic, suggestive, and explicit movement (17+ hours of video), a logistic regression trained on 61-feature LMA descriptors reaches 68% binary SFW/NSFW accuracy (70% random forest) under a leak-free evaluation protocol. At this level, our descriptor performs comparably to a learned video model trained on the same motion re-rendered as appearance-free video, a gray figure with no clothing, skin, or scene. The indirectness (tortuosity) of each joint's trajectory, measured as the ratio of the joint's path length to its net displacement, peaks at the suggestive tier, showing that the Direct-to-Indirect polarity of Laban's Space factor provides an interpretable marker of the shift from functional to suggestive motion. Ultimately, Laban-based kinematic descriptors offer a lightweight, interpretable approach to suggestive-motion detection: every decision decomposes into named, theory-grounded features. Because the classifier operates on pose trajectories alone, moderation can run directly on avatar poses in virtual environments, with no appearance data.

翻译：在线多人3D虚拟环境中的内容审核日趋自动化，但现有检测手段主要集中于图像、视频和音频，使得暗示性动作成为检测盲区。我们提出了一种纯运动分类管道，通过拉班运动分析（LMA）描述符从SMPL骨骼轨迹中检测暗示性及暴露性动作。在涵盖日常动作、艺术动作、暗示性动作和暴露性动作的数据集（17小时以上视频）中，基于61维LMA描述符的逻辑回归模型在无泄漏评估协议下达到了68%的二元安全/非安全准确率（随机森林模型为70%）。在此性能水平下，我们的描述符与基于同一动作渲染的无表观视频（无衣物、皮肤或场景的灰色人物剪影）训练的深度学习视频模型表现相当。各关节轨迹的间接性（曲折度）——即关节路径长度与净位移之比——在暗示性动作层级达到峰值，表明拉班空间因子中的直接-间接极性为功能性动作向暗示性动作的转变提供了可解释的标识。最终，基于拉班的运动学描述符为暗示性动作检测提供了一种轻量级、可解释的方案：每个检测决策均可分解为具有理论依据的命名特征。由于分类器仅依赖姿态轨迹运行，审核系统可直接在虚拟环境的虚拟角色姿态上执行，无需任何表观数据。