There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face reconstruction, they cannot easily capture geometric and appearance details. Methods based on neural implicit representations, such as signed distance functions (SDF) or neural radiance fields, approach photo-realism, but are difficult to animate and do not generalize well to unseen data. To tackle this problem, we propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Trained from a collection of high-quality 3D scans, our face model is parameterized by geometry, expression, and texture latent codes with a learned SDF and explicit UV texture parameterization. Once trained, we can reconstruct an avatar from a single in-the-wild image by leveraging the learned prior to project the image into the latent space of our model. Our implicit morphable face models can be used to render an avatar from novel views, animate facial expressions by modifying expression codes, and edit textures by directly painting on the learned UV-texture maps. We demonstrate quantitatively and qualitatively that our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.

翻译：随着高质量3D虚拟角色可动画化与可定制化的需求日益增长，尽管3D可变形模型为编辑和动画提供了直观控制，并具备单视图人脸重建的鲁棒性，但它们难以捕捉几何和外观细节。基于神经隐式表示的方法（如符号距离函数或神经辐射场）虽接近照片级真实感，但难以驱动且泛化能力不足。为解决此问题，我们提出一种新型隐式3D可变形人脸模型构建方法，兼具泛化性与直观编辑能力。该模型基于高质量3D扫描数据集训练，通过几何、表情和纹理潜在编码参数化，结合学习的SDF与显式UV纹理参数化。训练完成后，可利用学习先验将单张户外图像投影至模型潜在空间，从而重建数字头像。我们的隐式可变形人脸模型支持新视角渲染、通过修改表情编码驱动面部动画，以及直接在学习的UV纹理地图上进行纹理绘制编辑。定量与定性实验表明，与现有方法相比，本方法在照片级真实感、几何精度和表情准确性方面均有所提升。