发表机构
UCSC; UIUC; KAUST; Snap Inc.（加州大学圣克鲁兹分校; 伊利诺伊大学厄巴纳-香槟分校; 阿卜杜拉国王科技大学; Snap公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非拟人头部头像构建语义混合形状集成本高的问题，提出含大规模数据集、特定运动表示及快速前馈配准模型的RegHead框架，实验表明其生成表情网格保真度高、速度快，还能实现实时重定向。
AI 中文摘要
我们提出了RegHead，这是一个为可动画化的非拟人头部头像构建语义混合形状集的框架。语义混合形状提供了一个低维且可解释的动画接口，并支持跨身份重定向。构建这样的混合形状集成本高昂，原因包括缺乏一致表情监督、生成的4D资产缺乏对应关系以及面部运动高度局部化。我们提出了一个大规模数据集、一种针对局部面部变形的密集随机锚点运动表示以及一个快速前馈配准模型。实验表明，我们的方法能生成比基线更高保真的表情网格，且速度比优化方法快几个数量级。我们还展示了从人脸跟踪信号到非拟人角色的实时重定向，能捕捉头部姿势和局部面部运动。
英文摘要
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/.
