1,516 research outputs found
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder
that addresses the highly challenging problem of reconstructing a 3D human face
from a single in-the-wild color image. To this end, we combine a convolutional
encoder network with an expert-designed generative model that serves as
decoder. The core innovation is our new differentiable parametric decoder that
encapsulates image formation analytically based on a generative model. Our
decoder takes as input a code vector with exactly defined semantic meaning that
encodes detailed face pose, shape, expression, skin reflectance and scene
illumination. Due to this new way of combining CNN-based with model-based face
reconstruction, the CNN-based encoder learns to extract semantically meaningful
parameters from a single monocular input image. For the first time, a CNN
encoder and an expert-designed generative model can be trained end-to-end in an
unsupervised manner, which renders training on very large (unlabeled) real
world data feasible. The obtained reconstructions compare favorably to current
state-of-the-art approaches in terms of quality and richness of representation.Comment: International Conference on Computer Vision (ICCV) 2017 (Oral), 13
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Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Deep neural networks with alternating convolutional, max-pooling and
decimation layers are widely used in state of the art architectures for
computer vision. Max-pooling purposefully discards precise spatial information
in order to create features that are more robust, and typically organized as
lower resolution spatial feature maps. On some tasks, such as whole-image
classification, max-pooling derived features are well suited; however, for
tasks requiring precise localization, such as pixel level prediction and
segmentation, max-pooling destroys exactly the information required to perform
well. Precise localization may be preserved by shallow convnets without pooling
but at the expense of robustness. Can we have our max-pooled multi-layered cake
and eat it too? Several papers have proposed summation and concatenation based
methods for combining upsampled coarse, abstract features with finer features
to produce robust pixel level predictions. Here we introduce another model ---
dubbed Recombinator Networks --- where coarse features inform finer features
early in their formation such that finer features can make use of several
layers of computation in deciding how to use coarse features. The model is
trained once, end-to-end and performs better than summation-based
architectures, reducing the error from the previous state of the art on two
facial keypoint datasets, AFW and AFLW, by 30\% and beating the current
state-of-the-art on 300W without using extra data. We improve performance even
further by adding a denoising prediction model based on a novel convnet
formulation.Comment: accepted in CVPR 201
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.Comment: To be published in ICCV 201
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