We introduce Diffusion Parametric Head Models (DPHMs), a generative model
that enables robust volumetric head reconstruction and tracking from monocular
depth sequences. While recent volumetric head models, such as NPHMs, can now
excel in representing high-fidelity head geometries, tracking and
reconstruction heads from real-world single-view depth sequences remains very
challenging, as the fitting to partial and noisy observations is
underconstrained. To tackle these challenges, we propose a latent
diffusion-based prior to regularize volumetric head reconstruction and
tracking. This prior-based regularizer effectively constrains the identity and
expression codes to lie on the underlying latent manifold which represents
plausible head shapes. To evaluate the effectiveness of the diffusion-based
prior, we collect a dataset of monocular Kinect sequences consisting of various
complex facial expression motions and rapid transitions. We compare our method
to state-of-the-art tracking methods, and demonstrate improved head identity
reconstruction as well as robust expression tracking.

介绍了扩散参数头模型（DPHM），这是一个生成模型，可以从单目深度序列中实现强大的体积头部重建和跟踪。通过提出一种基于潜在扩散的先验进行规范化，有效地限制了身份和表情码位于表示合理头部形状的潜在流形上。通过与最先进的跟踪方法进行比较，证明了我们的方法在头部身份重建和表情跟踪方面的改进。