Skip to yearly menu bar Skip to main content


MonoNPHM: Dynamic Head Reconstruction from Monocular Videos

Simon Giebenhain · Tobias Kirschstein · Markos Georgopoulos · Martin Rünz · Lourdes Agapito · Matthias Nießner

Arch 4A-E Poster #106
award Highlight
[ ] [ Project Page ]
Thu 20 Jun 10:30 a.m. PDT — noon PDT


We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. To this end, we propose a latent appearance space that parameterizes a texture field on top of a neural parametric model. We constrain predicted color values to be correlated with the underlying geometry such that gradients from RGB effectively influence latent geometry codes during inverse rendering. To increase the representational capacity of our expression space, we augment our backward deformation field with hyper-dimensions, thus improving color and geometry representation in topologically challenging expressions. Using MonoNPHM as a learned prior, we approach the task of 3D head reconstruction using signed distance field based volumetric rendering. By numerically inverting our backward deformation field, we incorporated a landmark loss using facial anchor points that are closely tied to our canonical geometry representation. To evaluate the task of dynamic face reconstruction from monocular RGB videos we record 20 challenging Kinect sequences under casual conditions. MonoNPHM outperforms all baselines with a significant margin, and makes an important step towards easily accessible neural parametric face models through RGB tracking.

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