2,808 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
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial
section electron microscopy. Image analysis has been performed by manual labor
for half a century, and efforts at automation date back almost as far.
Convolutional nets were first applied to neuronal boundary detection a dozen
years ago, and have now achieved impressive accuracy on clean images. Robust
handling of image defects is a major outstanding challenge. Convolutional nets
are also being employed for other tasks in neural circuit reconstruction:
finding synapses and identifying synaptic partners, extending or pruning
neuronal reconstructions, and aligning serial section images to create a 3D
image stack. Computational systems are being engineered to handle petavoxel
images of cubic millimeter brain volumes
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