  We propose a 3D face generative model with local weights to increase the
model's variations and expressiveness. The proposed model allows partial
manipulation of the face while still learning the whole face mesh. For this
purpose, we address an effective way to extract local facial features from the
entire data and explore a way to manipulate them during a holistic generation.
First, we factorize the latent space of the whole face to the subspace
indicating different parts of the face. In addition, local weights generated by
non-negative matrix factorization are applied to the factorized latent space so
that the decomposed part space is semantically meaningful. We experiment with
our model and observe that effective facial part manipulation is possible and
that the model's expressiveness is improved.
