Appearance-Invariant Detection of Suggestive Motion via Laban Movement Descriptors on SMPL Skeletons
For content moderation in online multiplayer 3D virtual environments, this work provides a novel approach to detecting suggestive motion, addressing a blind spot in existing AI-based pipelines.
The paper presents a motion-only classification pipeline that detects suggestive and explicit movement from SMPL skeleton trajectories using Laban Movement Analysis (LMA) descriptors, achieving 57.3% four-way accuracy (2.3x chance) on 20,514 motion fragments.
Content moderation in online multiplayer 3D virtual environments has recently been relegated to automated, AI-based pipelines. However, the field has mainly been involved in detection of illicit content in images, video, and audio, leaving blind spots in detection techniques for suggestive motion. We present a motion-only classification pipeline that detects suggestive and explicit movement from SMPL skeleton trajectories using Laban Movement Analysis (LMA) descriptors. On 20,514 motion fragments (17+ hours) spanning four ordinal tiers -- everyday, artistic, suggestive, explicit -- logistic regression over 110 LMA features achieves 57.3% four-way accuracy (2.3x chance), 72.1% three-way, and 78.7% binary SFW/NSFW. Confusion concentrates on adjacent tiers, confirming that classification errors are concentrated between adjacent tiers over non-adjacent ones. Moreover, different movement qualities dominate at each level of the taxonomy -- no single feature drives the classification, suggesting that the four-tier structure reflects genuinely distinct motion regimes.