4,200 research outputs found
Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation
Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Learning to Reconstruct People in Clothing from a Single RGB Camera
We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Direct prediction of 3D body pose and shape remains a challenge even for
highly parameterized deep learning models. Mapping from the 2D image space to
the prediction space is difficult: perspective ambiguities make the loss
function noisy and training data is scarce. In this paper, we propose a novel
approach (Neural Body Fitting (NBF)). It integrates a statistical body model
within a CNN, leveraging reliable bottom-up semantic body part segmentation and
robust top-down body model constraints. NBF is fully differentiable and can be
trained using 2D and 3D annotations. In detailed experiments, we analyze how
the components of our model affect performance, especially the use of part
segmentations as an explicit intermediate representation, and present a robust,
efficiently trainable framework for 3D human pose estimation from 2D images
with competitive results on standard benchmarks. Code will be made available at
http://github.com/mohomran/neural_body_fittingComment: 3DV 201
FML: Face Model Learning from Videos
Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ,
Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19
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