RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration

We present RegHead, a framework for constructing semanticblendshape sets for animatable non-humanoid head avatars. With a fixedexpression vocabulary, semantic blendshapes provide a low-dimensionaland interpretable animation interface and support cross-identity retar-geting. Building such blendshape sets remains expensive because (i)expression-consistent supervision is scarce, (ii) generated 4D assets typ-ically lack correspondence, and (iii) facial motion is highly localized.We propose (1) a large-scale dataset of non-humanoid identities pairedwith a shared expression vocabulary, obtained by expanding a smallartist-rigged library via fine-tuned image editing; (2) a dense stochasticanchor motion representation tailored to localized facial deformations;and (3) a fast feed-forward registration model that converts unregis-tered expression meshes into a corresponded blendshape basis by pre-dicting anchor-based deformations from the neutral shape. Experimentsshow that our approach produces higher-fidelity expression meshes thanbaselines, while running orders of magnitude faster than optimization.We further demonstrate real-time retargeting from human face trackingsignals to non-humanoid characters, capturing both head pose and lo-calized facial motions. Our project page is available at https://snap-research.github.io/RegHead/.