Appearance-Invariant Detection of Suggestive Motion via Laban Movement Descriptors on SMPL Skeletons
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913159196966912 |
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| author | Ahn, Jaehoon Kong, Jeonghan Jung, Moon-Ryul |
| author_facet | Ahn, Jaehoon Kong, Jeonghan Jung, Moon-Ryul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24488 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Appearance-Invariant Detection of Suggestive Motion via Laban Movement Descriptors on SMPL Skeletons Ahn, Jaehoon Kong, Jeonghan Jung, Moon-Ryul Computer Vision and Pattern Recognition Graphics I.2.10; I.3.7 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. |
| title | Appearance-Invariant Detection of Suggestive Motion via Laban Movement Descriptors on SMPL Skeletons |
| topic | Computer Vision and Pattern Recognition Graphics I.2.10; I.3.7 |
| url | https://arxiv.org/abs/2605.24488 |