标题： GRMM：具有学习残差的实时高保真高斯可变形头部模型 显示英文标题

标题： GRMM: Real-Time High-Fidelity Gaussian Morphable Head Model with Learned Residuals

摘要： 3D可变形模型（3DMMs）使重建、动画和AR/VR中的可控面部几何和表情编辑成为可能，但传统的基于PCA的网格模型在分辨率、细节和逼真度方面存在限制。神经体积方法提高了逼真度，但对于交互使用仍然太慢。最近基于高斯点云（3DGS）的面部模型实现了快速、高质量的渲染，但仍然仅依赖于基于网格的3DMM先验进行表情控制，限制了它们捕捉细粒度几何、表情和全头覆盖的能力。我们引入了GRMM，这是第一个全头高斯3D可变形模型，它通过残差几何和外观组件增强了基础3DMM，这些附加改进可以恢复高频细节，如皱纹、精细皮肤纹理和发际线变化。GRMM通过低维、可解释的参数（例如，身份形状、面部表情）提供解耦控制，同时分别建模残差，以捕捉超出基础模型能力的受试者和表情特定的细节。粗略解码器生成顶点级网格变形，精细解码器表示每个高斯的外观，而一个轻量级CNN对光栅化图像进行细化以提高逼真度，同时保持75 FPS的实时渲染。为了学习一致的高保真残差，我们提出了EXPRESS-50，这是第一个包含50个身份上60个对齐表情的数据集，使得在基于高斯的3DMM中能够稳健地解耦身份和表情。在单目3D人脸重建、新视角合成和表情迁移方面，GRMM在保真度和表情准确性方面超越了最先进的方法，同时提供了交互式的实时性能。 显示英文摘要

摘要： 3D Morphable Models (3DMMs) enable controllable facial geometry and expression editing for reconstruction, animation, and AR/VR, but traditional PCA-based mesh models are limited in resolution, detail, and photorealism. Neural volumetric methods improve realism but remain too slow for interactive use. Recent Gaussian Splatting (3DGS) based facial models achieve fast, high-quality rendering but still depend solely on a mesh-based 3DMM prior for expression control, limiting their ability to capture fine-grained geometry, expressions, and full-head coverage. We introduce GRMM, the first full-head Gaussian 3D morphable model that augments a base 3DMM with residual geometry and appearance components, additive refinements that recover high-frequency details such as wrinkles, fine skin texture, and hairline variations. GRMM provides disentangled control through low-dimensional, interpretable parameters (e.g., identity shape, facial expressions) while separately modelling residuals that capture subject- and expression-specific detail beyond the base model's capacity. Coarse decoders produce vertex-level mesh deformations, fine decoders represent per-Gaussian appearance, and a lightweight CNN refines rasterised images for enhanced realism, all while maintaining 75 FPS real-time rendering. To learn consistent, high-fidelity residuals, we present EXPRESS-50, the first dataset with 60 aligned expressions across 50 identities, enabling robust disentanglement of identity and expression in Gaussian-based 3DMMs. Across monocular 3D face reconstruction, novel-view synthesis, and expression transfer, GRMM surpasses state-of-the-art methods in fidelity and expression accuracy while delivering interactive real-time performance.