Authors:
Yajie Gu
and
Nick Pears
Affiliation:
VGL Research Group, Department of Computer Science, University of York, YO10 5GH, U.K.
Keyword(s):
Face Modeling, Deformation Network, Parts Corresponding Implicit Representations, Signed Distance Functions.
Abstract:
Previous 3D face analysis has focussed on 3D facial identity, expression and pose disentanglement. However, the independent control of different facial parts and the ability to learn explainable parts-based latent shape embeddings for implicit surfaces remain as open problems. We propose a method for 3D face modeling that learns a continuous parts-based deformation field that maps the various semantic parts of a subject’s face to a template. By swapping affine-mapped facial features among different individuals from predefined regions we achieve significant parts-based training data augmentation. Moreover, by sequentially morphing the surface points of these parts, we learn corresponding latent representations, shape deformation fields, and the signed distance function of a template shape. This gives improved shape controllability and better interpretability of the face latent space, while retaining all of the known advantages of implicit surface modelling. Unlike previous works that
generated new faces based on full-identity latent representations, our approach enables independent control of different facial parts, i.e. nose, mouth, eyes and also the remaining surface and yet generates new faces with high reconstruction quality. Evaluations demonstrate both facial expression and parts disentanglement, independent control of those facial parts, as well as state-of-the art facial parts reconstruction, when evaluated on FaceScape and Headspace datasets.
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