Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements
Effective human behavior modeling requires a representation of the human body movement that capitalizes on its compositionality. We propose a hierarchical representation consisting of Action Atoms that capture the atomic joint movements and Action Motifs that are formed by their temporal compositions and encode similar body movements found across different overall human actions. We derive A4Mer, a nested latent Transformer to learn this hierarchical representation from human pose data in a fully self-supervised manner. A4Mer splits a 3D pose sequence into variable-length segments and represents each segment as a single latent token (Action Atoms). Through bottom-up representation learning...
| Authors | Genki Kinoshita, Shu Nakamura, Ryo Kawahara et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 April 2026 |
| Last updated at source | 2 June 2026 |
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