64,928 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
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians
Convolutional neural nets (CNNs) have demonstrated remarkable performance in
recent history. Such approaches tend to work in a unidirectional bottom-up
feed-forward fashion. However, practical experience and biological evidence
tells us that feedback plays a crucial role, particularly for detailed spatial
understanding tasks. This work explores bidirectional architectures that also
reason with top-down feedback: neural units are influenced by both lower and
higher-level units.
We do so by treating units as rectified latent variables in a quadratic
energy function, which can be seen as a hierarchical Rectified Gaussian model
(RGs). We show that RGs can be optimized with a quadratic program (QP), that
can in turn be optimized with a recurrent neural network (with rectified linear
units). This allows RGs to be trained with GPU-optimized gradient descent. From
a theoretical perspective, RGs help establish a connection between CNNs and
hierarchical probabilistic models. From a practical perspective, RGs are well
suited for detailed spatial tasks that can benefit from top-down reasoning. We
illustrate them on the challenging task of keypoint localization under
occlusions, where local bottom-up evidence may be misleading. We demonstrate
state-of-the-art results on challenging benchmarks.Comment: To appear in CVPR 201
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Detection and segmentation of objects in overheard imagery is a challenging
task. The variable density, random orientation, small size, and
instance-to-instance heterogeneity of objects in overhead imagery calls for
approaches distinct from existing models designed for natural scene datasets.
Though new overhead imagery datasets are being developed, they almost
universally comprise a single view taken from directly overhead ("at nadir"),
failing to address a critical variable: look angle. By contrast, views vary in
real-world overhead imagery, particularly in dynamic scenarios such as natural
disasters where first looks are often over 40 degrees off-nadir. This
represents an important challenge to computer vision methods, as changing view
angle adds distortions, alters resolution, and changes lighting. At present,
the impact of these perturbations for algorithmic detection and segmentation of
objects is untested. To address this problem, we present an open source
Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks
from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of
these images cover the same 665 square km geographic extent and are annotated
with 126,747 building footprint labels, enabling direct assessment of the
impact of viewpoint perturbation on model performance. We benchmark multiple
leading segmentation and object detection models on: (1) building detection,
(2) generalization to unseen viewing angles and resolutions, and (3)
sensitivity of building footprint extraction to changes in resolution. We find
that state of the art segmentation and object detection models struggle to
identify buildings in off-nadir imagery and generalize poorly to unseen views,
presenting an important benchmark to explore the broadly relevant challenge of
detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV)
201
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