Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map

Zhenfeng Fan, Zhiheng Zhang, Shuang Yang, Chongyang Zhong, Min Cao, Shihong Xia

Abstract:

While impressive progress has recently been made in
image-oriented facial attribute translation, shape-oriented
3D facial attribute translation remains an unsolved issue.
This is primarily limited by the lack of 3D generative models and ineffective usage of 3D facial data. We propose
a learning framework for 3D facial attribute translation
to relieve these limitations. Firstly, we customize a novel
geometric map for 3D shape representation and embed
it in an end-to-end generative adversarial network. The
geometric map represents 3D shapes symmetrically on a
square image grid, while preserving the neighboring relationship of 3D vertices in a local least-square sense. This
enables effective learning for the latent representation of
data with different attributes. Secondly, we employ a unified and unpaired learning framework for multi-domain attribute translation. It not only makes effective usage of
data correlation from multiple domains, but also mitigates
the constraint for hardly accessible paired data. Finally,
we propose a hierarchical architecture for the discriminator to guarantee robust results against both global and local artifacts. We conduct extensive experiments to demonstrate the advantage of the proposed framework over the
state-of-the-art in generating high-fidelity facial shapes.
Given an input 3D facial shape, the proposed framework
is able to synthesize novel shapes of different attributes,
which covers some downstream applications, such as expression transfer, gender translation, and aging.