学习可动画高斯头部虚拟形象的面部语义修复

中文摘要 AI 辅助

我们提出了SInGA，一种从单张图像学习可动画高斯头部虚拟形象的面部语义修复的新方法。现有的虚拟形象方法通常依赖多视角观察，并且在单视角设置中缺乏对未观察区域的有效处理，限制了它们在此类场景中的适用性。为解决这一问题，我们提出了一个在UV空间中定义的语义修复框架，用于补全未观察的面部区域。我们的关键洞察在于UV表示的结构化拓扑，它提供了一致的空间对应关系，并利用人类面部的固有对称性线索，能够可靠地补全身份特定的特征。我们从观察到的区域提取特征，并使用这些特征来补全未观察的区域。补全后的表示随后用于回归高斯属性，有效地执行高斯修复。此外，我们不在每个表面或像素位置仅使用单个高斯，而是堆叠多个高斯以增强细节。由此产生的虚拟形象无需针对每个身份进行优化即可跨身份泛化，并且可以使用驱动输入进行动画化。实验结果表明，我们的方法生成了高质量的头部虚拟形象，具有更好的完整性和身份保持性，同时支持逼真的动画和从未观察视角的一致渲染。

英文摘要

We present SInGA, a novel method for learning Semantic Inpainting for animatable Gaussian head Avatars from a single image. Existing avatar approaches often rely on multi-view observations and lack effective handling of unobserved regions in single-view settings, limiting their applicability in such scenarios. To address this, we propose a semantic inpainting framework defined in UV space for completing unobserved facial regions. Our key insight lies in the structured topology of the UV representation, which provides consistent spatial correspondences and enables reliable completion of identity-specific features using the inherent symmetry cues of human faces. We extract features from observed regions and use them to complete unobserved regions. The completed representation is then used to regress Gaussian attributes, effectively performing Gaussian inpainting. In addition, instead of relying on a single Gaussian at each surface or pixel location, we stack multiple Gaussians to enhance detail. The resulting avatar generalizes across identities without requiring per-identity optimization and can be animated with driving inputs. Experimental results show that our method generates high-quality head avatars with improved completeness and identity preservation, while supporting realistic animation and consistent rendering from unobserved views.

发表机构

• Michigan State University（密歇根州立大学）

机构由 AI 辅助整理，请以论文原文为准。