{"ID":6621298,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12206","arxiv_id":"2607.12206","title":"RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration","abstract":"We present RegHead, a framework for constructing semantic blendshape sets for animatable non-humanoid head avatars. With a fixed expression vocabulary, semantic blendshapes provide a low-dimensional and interpretable animation interface and support cross-identity retargeting. Building such blendshape sets remains expensive because (i) expression-consistent supervision is scarce, (ii) generated 4D assets typically lack correspondence, and (iii) facial motion is highly localized. We propose (1) a large-scale dataset of non-humanoid identities paired with a shared expression vocabulary, obtained by expanding a small artist-rigged library via fine-tuned image editing; (2) a dense stochastic anchor motion representation tailored to localized facial deformations; and (3) a fast feed-forward registration model that converts unregistered expression meshes into a corresponded blendshape basis by predicting anchor-based deformations from the neutral shape. Experiments show that our approach produces higher-fidelity expression meshes than baselines, while running orders of magnitude faster than optimization. We further demonstrate real-time retargeting from human face tracking signals to non-humanoid characters, capturing both head pose and localized facial motions. Our project page is available at https://snap-research.github.io/RegHead/.","short_abstract":"We present RegHead, a framework for constructing semantic blendshape sets for animatable non-humanoid head avatars. With a fixed expression vocabulary, semantic blendshapes provide a low-dimensional and interpretable animation interface and support cross-identity retargeting. Building such blendshape sets remains expen...","url_abs":"https://arxiv.org/abs/2607.12206","url_pdf":"https://arxiv.org/pdf/2607.12206v1","authors":"[\"Jiahao Luo\",\"Hao Zhang\",\"Jianqi Chen\",\"Yijie He\",\"Jiaxu Zou\",\"Michael Vasilkovsky\",\"Sergei Korolev\",\"Sergey Tulyakov\",\"Chaoyang Wang\",\"Peter Wonka\",\"James Davis\",\"Jian Wang\"]","published":"2026-07-13T23:16:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.GR\"]","methods":"[]","has_code":false}