In this work, we present a new method for 3D face reconstruction from
sparse-view RGB images. Unlike previous methods which are built upon 3D
morphable models (3DMMs) with limited details, we leverage an implicit
representation to encode rich geometric features. Our overall pipeline consists
of two major components, including a geometry network, which learns a
deformable neural signed distance function (SDF) as the 3D face representation,
and a rendering network, which learns to render on-surface points of the neural
SDF to match the input images via self-supervised optimization. To handle
in-the-wild sparse-view input of the same target with different expressions at
test time, we propose residual latent code to effectively expand the shape
space of the learned implicit face representation as well as a novel
view-switch loss to enforce consistency among different views. Our experimental
results on several benchmark datasets demonstrate that our approach outperforms
alternative baselines and achieves superior face reconstruction results
compared to state-of-the-art methods.Comment: 10 pages, 6 figures, The 30th Pacific Conference on Computer Graphics
and Applications. Pacific Graphics(PG) 202