MonoNPHM: Dynamic Head Reconstruction from Monocular Videos
Simon Giebenhain Tobias Kirschstein Markos Georgopoulos Martin Rünz Lourdes Agapito Matthias Nießner
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.