  Reconstructing 3D human heads in low-view settings presents technical
challenges, mainly due to the pronounced risk of overfitting with limited views
and high-frequency signals. To address this, we propose geometry decomposition
and adopt a two-stage, coarse-to-fine training strategy, allowing for
progressively capturing high-frequency geometric details. We represent 3D human
heads using the zero level-set of a combined signed distance field, comprising
a smooth template, a non-rigid deformation, and a high-frequency displacement
field. The template captures features that are independent of both identity and
expression and is co-trained with the deformation network across multiple
individuals with sparse and randomly selected views. The displacement field,
capturing individual-specific details, undergoes separate training for each
person. Our network training does not require 3D supervision or object masks.
Experimental results demonstrate the effectiveness and robustness of our
geometry decomposition and two-stage training strategy. Our method outperforms
existing neural rendering approaches in terms of reconstruction accuracy and
novel view synthesis under low-view settings. Moreover, the pre-trained
template serves a good initialization for our model when encountering unseen
individuals.
