Retargeting Motions to Diverse Skeletons via Learnable Flattening
Quick summary
arXiv:2609.38578v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2609.38578v1 Announce Type: cross Abstract: 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.
Why it matters
The importance of “Retargeting Motions to Diverse Skeletons via Learnable Flattening” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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