Retargeting Motions to Diverse Skeletons via Learnable Flattening
Cross-structural motion retargeting aims to transfer motion between different skeletal topologies. Despite recent progress, existing state-of-the-art models struggle with reliability in zero-shot settings, i.e. skeletons with different topologies which were unseen during training, and recent Transformer-based attempts have failed to outperform specialized geometric methods. We bridge this gap with a Transformer Autoencoder that learns a topology- and translation-invariant latent space. Our core contribution is a learnable flattening of skeletal graphs that captures both local dependencies and global structure. Unlike the standard transformer architecture, which adds positional information...
| Authors | Kia-Jüng Yang, Fabian H. Sinz, Paweł A. Pierzchlewicz |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 1 October 2026 |
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