Abstract: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.
| Comments: | 2 pages, 2 figures. Accepted as a Poster at SIGGRAPH 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) |
| ACM classes: | I.2.10; I.3.7 |
| Cite as: | arXiv:2605.24488 [cs.CV] |
| (or arXiv:2605.24488v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24488 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1145/3799825.3818709
DOI(s) linking to related resources |
From: Jaehoon Ahn [view email]
[v1]
Sat, 23 May 2026 09:22:32 UTC (69 KB)
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