The remarkable progress in 3D face reconstruction has resulted in high-detail
and photorealistic facial representations. Recently, Diffusion Models have
revolutionized the capabilities of generative methods by achieving far better
performance than GANs. In this work, we present FitDiff, a diffusion-based 3D
facial avatar generative model. This model accurately generates relightable
facial avatars, utilizing an identity embedding extracted from an "in-the-wild"
2D facial image. Our multi-modal diffusion model concurrently outputs facial
reflectance maps (diffuse and specular albedo and normals) and shapes,
showcasing great generalization capabilities. It is solely trained on an
annotated subset of a public facial dataset, paired with 3D reconstructions. We
revisit the typical 3D facial fitting approach by guiding a reverse diffusion
process using perceptual and face recognition losses. Being the first LDM
conditioned on face recognition embeddings, FitDiff reconstructs relightable
human avatars, that can be used as-is in common rendering engines, starting
only from an unconstrained facial image, and achieving state-of-the-art
performance.

在这项研究中，我们提出了 FitDiff，一种基于扩散的三维人脸化身生成模型，它可准确生成可光照补充的人脸化身，利用从 “野外” 二维人脸图像中提取的身份嵌入。该多模态扩散模型同时输出面部反射映射（漫反射和镜面反射率及法线）和形状，展示了强大的泛化能力。它仅在公共人脸数据集的注释子集上进行训练，与三维重建配对。我们通过使用感知和人脸识别损失来指导反向扩散过程，重新审视了典型的三维面部拟合方法。FitDiff 是第一个以面部识别嵌入为条件的 LDM，它能够重构可光照补充的人类化身，可以直接在常见的渲染引擎中使用，仅从无约束的人脸图像开始，并达到了业界的最佳性能。

FitDiff：使用扩散模型进行鲁棒的单目 3D 面部形状和反射估计

FitDiff: Robust monocular 3D facial shape and reflectance estimation  using Diffusion Models

3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D
imagery. Towards fine-grained control over facial attributes, recent efforts
incorporate 3D Morphable Face Model (3DMM) to describe deformation in
generative radiance fields either explicitly or implicitly. Explicit methods
provide fine-grained expression control but cannot handle topological changes
caused by hair and accessories, while implicit ones can model varied topologies
but have limited generalization caused by the unconstrained deformation fields.
We propose a novel 3D GAN framework for unsupervised learning of generative,
high-quality and 3D-consistent facial avatars from unstructured 2D images. To
achieve both deformation accuracy and topological flexibility, we propose a 3D
representation called Generative Texture-Rasterized Tri-planes. The proposed
representation learns Generative Neural Textures on top of parametric mesh
templates and then projects them into three orthogonal-viewed feature planes
through rasterization, forming a tri-plane feature representation for volume
rendering. In this way, we combine both fine-grained expression control of
mesh-guided explicit deformation and the flexibility of implicit volumetric
representation. We further propose specific modules for modeling mouth interior
which is not taken into account by 3DMM. Our method demonstrates
state-of-the-art 3D-aware synthesis quality and animation ability through
extensive experiments. Furthermore, serving as 3D prior, our animatable 3D
representation boosts multiple applications including one-shot facial avatars
and 3D-aware stylization.

本文提出了一种使用 3D GAN 生成高保真的、多视角一致的面部图像的方法，可以通过不受限制的变形场实现细粒度的表情控制和拓扑灵活性，并在口腔内部方面进行特定模块的建模来提高动画能力，这种 3D 表示还可以作为 3D 先验对于多种应用具有提升作用。