8,389 research outputs found
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map
Most of the existing deep learning-based methods for 3D hand and human pose
estimation from a single depth map are based on a common framework that takes a
2D depth map and directly regresses the 3D coordinates of keypoints, such as
hand or human body joints, via 2D convolutional neural networks (CNNs). The
first weakness of this approach is the presence of perspective distortion in
the 2D depth map. While the depth map is intrinsically 3D data, many previous
methods treat depth maps as 2D images that can distort the shape of the actual
object through projection from 3D to 2D space. This compels the network to
perform perspective distortion-invariant estimation. The second weakness of the
conventional approach is that directly regressing 3D coordinates from a 2D
image is a highly non-linear mapping, which causes difficulty in the learning
procedure. To overcome these weaknesses, we firstly cast the 3D hand and human
pose estimation problem from a single depth map into a voxel-to-voxel
prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood
for each keypoint. We design our model as a 3D CNN that provides accurate
estimates while running in real-time. Our system outperforms previous methods
in almost all publicly available 3D hand and human pose estimation datasets and
placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge.
The code is available in https://github.com/mks0601/V2V-PoseNet_RELEASE.Comment: HANDS 2017 Challenge Frame-based 3D Hand Pose Estimation Winner (ICCV
2017), Published at CVPR 201
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Group emotion recognition in the wild is a challenging problem, due to the
unstructured environments in which everyday life pictures are taken. Some of
the obstacles for an effective classification are occlusions, variable lighting
conditions, and image quality. In this work we present a solution based on a
novel combination of deep neural networks and Bayesian classifiers. The neural
network works on a bottom-up approach, analyzing emotions expressed by isolated
faces. The Bayesian classifier estimates a global emotion integrating top-down
features obtained through a scene descriptor. In order to validate the system
we tested the framework on the dataset released for the Emotion Recognition in
the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test
set, significantly outperforming the 53.62% competition baseline.Comment: accepted by the Fifth Emotion Recognition in the Wild (EmotiW)
Challenge 201
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