  Generative 3D face models featuring disentangled controlling factors hold
immense potential for diverse applications in computer vision and computer
graphics. However, previous 3D face modeling methods face a challenge as they
demand specific labels to effectively disentangle these factors. This becomes
particularly problematic when integrating multiple 3D face datasets to improve
the generalization of the model. Addressing this issue, this paper introduces a
Weakly-Supervised Disentanglement Framework, denoted as WSDF, to facilitate the
training of controllable 3D face models without an overly stringent labeling
requirement. Adhering to the paradigm of Variational Autoencoders (VAEs), the
proposed model achieves disentanglement of identity and expression controlling
factors through a two-branch encoder equipped with dedicated
identity-consistency prior. It then faithfully re-entangles these factors via a
tensor-based combination mechanism. Notably, the introduction of the Neutral
Bank allows precise acquisition of subject-specific information using only
identity labels, thereby averting degeneration due to insufficient supervision.
Additionally, the framework incorporates a label-free second-order loss
function for the expression factor to regulate deformation space and eliminate
extraneous information, resulting in enhanced disentanglement. Extensive
experiments have been conducted to substantiate the superior performance of
WSDF. Our code is available at https://github.com/liguohao96/WSDF.
