20 research outputs found
Self-supervised learning of a facial attribute embedding from video
We propose a self-supervised framework for learning facial attributes by
simply watching videos of a human face speaking, laughing, and moving over
time. To perform this task, we introduce a network, Facial Attributes-Net
(FAb-Net), that is trained to embed multiple frames from the same video
face-track into a common low-dimensional space. With this approach, we make
three contributions: first, we show that the network can leverage information
from multiple source frames by predicting confidence/attention masks for each
frame; second, we demonstrate that using a curriculum learning regime improves
the learned embedding; finally, we demonstrate that the network learns a
meaningful face embedding that encodes information about head pose, facial
landmarks and facial expression, i.e. facial attributes, without having been
supervised with any labelled data. We are comparable or superior to
state-of-the-art self-supervised methods on these tasks and approach the
performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at
http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true diversity in facial geometry, skin reflectance and illumination. To
alleviate this problem, we present the first approach that jointly learns 1) a
regressor for face shape, expression, reflectance and illumination on the basis
of 2) a concurrently learned parametric face model. Our multi-level face model
combines the advantage of 3D Morphable Models for regularization with the
out-of-space generalization of a learned corrective space. We train end-to-end
on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and a self-supervised
training loss, both defined at multiple detail levels. Our approach compares
favorably to the state-of-the-art in terms of reconstruction quality, better
generalizes to real world faces, and runs at over 250 Hz.Comment: CVPR 2018 (Oral). Project webpage:
https://gvv.mpi-inf.mpg.de/projects/FML