Skip to yearly menu bar Skip to main content


DPHMs: Diffusion Parametric Head Models for Depth-based Tracking

Jiapeng Tang · Angela Dai · Yinyu Nie · Lev Markhasin · Justus Thies · Matthias Nie├čner

Arch 4A-E Poster #93
[ ] [ Project Page ]
Wed 19 Jun 10:30 a.m. PDT — noon PDT


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 reconstructing 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.

Live content is unavailable. Log in and register to view live content