39 research outputs found
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material
(http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a
person's face given an input face photograph. This is achieved by learning to
generate facial landmarks and textures from features extracted from a
facial-recognition network. Unlike previous approaches, our encoding feature
vector is largely invariant to lighting, pose, and facial expression.
Exploiting this invariance, we train our decoder network using only frontal,
neutral-expression photographs. Since these photographs are well aligned, we
can decompose them into a sparse set of landmark points and aligned texture
maps. The decoder then predicts landmarks and textures independently and
combines them using a differentiable image warping operation. The resulting
images can be used for a number of applications, such as analyzing facial
attributes, exposure and white balance adjustment, or creating a 3-D avatar
OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
Contrails (condensation trails) are line-shaped ice clouds caused by aircraft
and are likely the largest contributor of aviation-induced climate change.
Contrail avoidance is potentially an inexpensive way to significantly reduce
the climate impact of aviation. An automated contrail detection system is an
essential tool to develop and evaluate contrail avoidance systems. In this
paper, we present a human-labeled dataset named OpenContrails to train and
evaluate contrail detection models based on GOES-16 Advanced Baseline Imager
(ABI) data. We propose and evaluate a contrail detection model that
incorporates temporal context for improved detection accuracy. The human
labeled dataset and the contrail detection outputs are publicly available on
Google Cloud Storage at gs://goes_contrails_dataset
A scalable system to measure contrail formation on a per-flight basis
Persistent contrails make up a large fraction of aviation's contribution to
global warming. We describe a scalable, automated detection and matching (ADM)
system to determine from satellite data whether a flight has made a persistent
contrail. The ADM system compares flight segments to contrails detected by a
computer vision algorithm running on images from the GOES-16 Advanced Baseline
Imager. We develop a 'flight matching' algorithm and use it to label each
flight segment as a 'match' or 'non-match'. We perform this analysis on 1.6
million flight segments. The result is an analysis of which flights make
persistent contrails several orders of magnitude larger than any previous work.
We assess the agreement between our labels and available prediction models
based on weather forecasts. Shifting air traffic to avoid regions of contrail
formation has been proposed as a possible mitigation with the potential for
very low cost/ton-CO2e. Our findings suggest that imperfections in these
prediction models increase this cost/ton by about an order of magnitude.
Contrail avoidance is a cost-effective climate change mitigation even with this
factor taken into account, but our results quantify the need for more accurate
contrail prediction methods and establish a benchmark for future development.Comment: 25 pages, 6 figure
Synthesizing Normalized Faces from Facial Identity Features
© 2017 IEEE. We present a method for synthesizing a frontal, neutralexpression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar