We propose ID-to-3D, a method to generate identity- and text-guided 3D human
heads with disentangled expressions, starting from even a single casually
captured in-the-wild image of a subject. The foundation of our approach is
anchored in compositionality, alongside the use of task-specific 2D diffusion
models as priors for optimization. First, we extend a foundational model with a
lightweight expression-aware and ID-aware architecture, and create 2D priors
for geometry and texture generation, via fine-tuning only 0.2% of its available
training parameters. Then, we jointly leverage a neural parametric
representation for the expressions of each subject and a multi-stage generation
of highly detailed geometry and albedo texture. This combination of strong face
identity embeddings and our neural representation enables accurate
reconstruction of not only facial features but also accessories and hair and
can be meshed to provide render-ready assets for gaming and telepresence. Our
results achieve an unprecedented level of identity-consistent and high-quality
texture and geometry generation, generalizing to a ``world'' of unseen 3D
identities, without relying on large 3D captured datasets of human assets.

我们提出了 ID-to-3D，这是一种方法，可以通过从一个随意拍摄的野外图像中生成具有解耦表情的身份和文本引导的 3D 人头。我们的方法基于组合性，并使用特定任务的 2D 扩散模型作为优化的先验知识。我们的结果实现了前所未有的一致性身份和高质量纹理和几何生成，可以用于游戏和远程呈现。

ID-to-3D：通过分数蒸馏采样表达个体身份引导的 3D 头部

ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling

Recent studies on facial expression editing have obtained very promising
progress. On the other hand, existing methods face the constraint of requiring
a large amount of expression labels which are often expensive and
time-consuming to collect. This paper presents an innovative label-free
expression editing via disentanglement (LEED) framework that is capable of
editing the expression of both frontal and profile facial images without
requiring any expression label. The idea is to disentangle the identity and
expression of a facial image in the expression manifold, where the neutral face
captures the identity attribute and the displacement between the neutral image
and the expressive image captures the expression attribute. Two novel losses
are designed for optimal expression disentanglement and consistent synthesis,
including a mutual expression information loss that aims to extract pure
expression-related features and a siamese loss that aims to enhance the
expression similarity between the synthesized image and the reference image.
Extensive experiments over two public facial expression datasets show that LEED
achieves superior facial expression editing qualitatively and quantitatively.

本文提出了一个用于面部表情编辑的创新无标注离散编辑（LEED）框架，能够在不需要任何表情标签的情况下对正面和侧面面部图像的表情进行编辑，并设计了两种新的损失以实现最佳的表情剥离和一致性合成，经过两个公共面部表情数据集的大量实验，LEED 实现了优越的面部表情编辑质量。