795 research outputs found
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 201
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
Convolutional Neural Network based approaches for monocular 3D human pose
estimation usually require a large amount of training images with 3D pose
annotations. While it is feasible to provide 2D joint annotations for large
corpora of in-the-wild images with humans, providing accurate 3D annotations to
such in-the-wild corpora is hardly feasible in practice. Most existing 3D
labelled data sets are either synthetically created or feature in-studio
images. 3D pose estimation algorithms trained on such data often have limited
ability to generalize to real world scene diversity. We therefore propose a new
deep learning based method for monocular 3D human pose estimation that shows
high accuracy and generalizes better to in-the-wild scenes. It has a network
architecture that comprises a new disentangled hidden space encoding of
explicit 2D and 3D features, and uses supervision by a new learned projection
model from predicted 3D pose. Our algorithm can be jointly trained on image
data with 3D labels and image data with only 2D labels. It achieves
state-of-the-art accuracy on challenging in-the-wild data.Comment: Accepted to CVPR 201
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data
Estimation of Absolute Scale in Monocular SLAM Using Synthetic Data
This paper addresses the problem of scale estimation in monocular SLAM by
estimating absolute distances between camera centers of consecutive image
frames. These estimates would improve the overall performance of classical (not
deep) SLAM systems and allow metric feature locations to be recovered from a
single monocular camera. We propose several network architectures that lead to
an improvement of scale estimation accuracy over the state of the art. In
addition, we exploit a possibility to train the neural network only with
synthetic data derived from a computer graphics simulator. Our key insight is
that, using only synthetic training inputs, we can achieve similar scale
estimation accuracy as that obtained from real data. This fact indicates that
fully annotated simulated data is a viable alternative to existing
deep-learning-based SLAM systems trained on real (unlabeled) data. Our
experiments with unsupervised domain adaptation also show that the difference
in visual appearance between simulated and real data does not affect scale
estimation results. Our method operates with low-resolution images (0.03MP),
which makes it practical for real-time SLAM applications with a monocular
camera
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