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。