1,265 research outputs found
Large pose 3D face reconstruction from a single image via direct volumetric CNN regression
3D face reconstruction is a fundamental Computer Vision problem of
extraordinary difficulty. Current systems often assume the availability of
multiple facial images (sometimes from the same subject) as input, and must
address a number of methodological challenges such as establishing dense
correspondences across large facial poses, expressions, and non-uniform
illumination. In general these methods require complex and inefficient
pipelines for model building and fitting. In this work, we propose to address
many of these limitations by training a Convolutional Neural Network (CNN) on
an appropriate dataset consisting of 2D images and 3D facial models or scans.
Our CNN works with just a single 2D facial image, does not require accurate
alignment nor establishes dense correspondence between images, works for
arbitrary facial poses and expressions, and can be used to reconstruct the
whole 3D facial geometry (including the non-visible parts of the face)
bypassing the construction (during training) and fitting (during testing) of a
3D Morphable Model. We achieve this via a simple CNN architecture that performs
direct regression of a volumetric representation of the 3D facial geometry from
a single 2D image. We also demonstrate how the related task of facial landmark
localization can be incorporated into the proposed framework and help improve
reconstruction quality, especially for the cases of large poses and facial
expressions. Testing code will be made available online, along with pre-trained
models http://aaronsplace.co.uk/papers/jackson2017reconComment: 10 pages, ICCV 201
BodyNet: Volumetric Inference of 3D Human Body Shapes
Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. In this
work we argue for an alternative representation and propose BodyNet, a neural
network for direct inference of volumetric body shape from a single image.
BodyNet is an end-to-end trainable network that benefits from (i) a volumetric
3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate
supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them
results in performance improvement as demonstrated by our experiments. To
evaluate the method, we fit the SMPL model to our network output and show
state-of-the-art results on the SURREAL and Unite the People datasets,
outperforming recent approaches. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.Comment: Appears in: European Conference on Computer Vision 2018 (ECCV 2018).
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