1,222 research outputs found
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
Face pose estimation in monocular images
People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area.
In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc.
Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Traditional approaches to upper body pose estimation using monocular vision
rely on complex body models and a large variety of geometric constraints. We
argue that this is not ideal and somewhat inelegant as it results in large
processing burdens, and instead attempt to incorporate these constraints
through priors obtained directly from training data. A prior distribution
covering the probability of a human pose occurring is used to incorporate
likely human poses. This distribution is obtained offline, by fitting a
Gaussian mixture model to a large dataset of recorded human body poses, tracked
using a Kinect sensor. We combine this prior information with a random walk
transition model to obtain an upper body model, suitable for use within a
recursive Bayesian filtering framework. Our model can be viewed as a mixture of
discrete Ornstein-Uhlenbeck processes, in that states behave as random walks,
but drift towards a set of typically observed poses. This model is combined
with measurements of the human head and hand positions, using recursive
Bayesian estimation to incorporate temporal information. Measurements are
obtained using face detection and a simple skin colour hand detector, trained
using the detected face. The suggested model is designed with analytical
tractability in mind and we show that the pose tracking can be
Rao-Blackwellised using the mixture Kalman filter, allowing for computational
efficiency while still incorporating bio-mechanical properties of the upper
body. In addition, the use of the proposed upper body model allows reliable
three-dimensional pose estimates to be obtained indirectly for a number of
joints that are often difficult to detect using traditional object recognition
strategies. Comparisons with Kinect sensor results and the state of the art in
2D pose estimation highlight the efficacy of the proposed approach.Comment: 25 pages, Technical report, related to Burke and Lasenby, AMDO 2014
conference paper. Code sample: https://github.com/mgb45/SignerBodyPose Video:
https://www.youtube.com/watch?v=dJMTSo7-uF
Relative Facial Action Unit Detection
This paper presents a subject-independent facial action unit (AU) detection
method by introducing the concept of relative AU detection, for scenarios where
the neutral face is not provided. We propose a new classification objective
function which analyzes the temporal neighborhood of the current frame to
decide if the expression recently increased, decreased or showed no change.
This approach is a significant change from the conventional absolute method
which decides about AU classification using the current frame, without an
explicit comparison with its neighboring frames. Our proposed method improves
robustness to individual differences such as face scale and shape, age-related
wrinkles, and transitions among expressions (e.g., lower intensity of
expressions). Our experiments on three publicly available datasets (Extended
Cohn-Kanade (CK+), Bosphorus, and DISFA databases) show significant improvement
of our approach over conventional absolute techniques. Keywords: facial action
coding system (FACS); relative facial action unit detection; temporal
information;Comment: Accepted at IEEE Winter Conference on Applications of Computer
Vision, Steamboat Springs Colorado, USA, 201
A Generative Model of People in Clothing
We present the first image-based generative model of people in clothing for
the full body. We sidestep the commonly used complex graphics rendering
pipeline and the need for high-quality 3D scans of dressed people. Instead, we
learn generative models from a large image database. The main challenge is to
cope with the high variance in human pose, shape and appearance. For this
reason, pure image-based approaches have not been considered so far. We show
that this challenge can be overcome by splitting the generating process in two
parts. First, we learn to generate a semantic segmentation of the body and
clothing. Second, we learn a conditional model on the resulting segments that
creates realistic images. The full model is differentiable and can be
conditioned on pose, shape or color. The result are samples of people in
different clothing items and styles. The proposed model can generate entirely
new people with realistic clothing. In several experiments we present
encouraging results that suggest an entirely data-driven approach to people
generation is possible
Stereo Pictorial Structure for 2D Articulated Human Pose Estimation
In this paper, we consider the problem of 2D human
pose estimation on stereo image pairs. In particular,
we aim at estimating the location, orientation and scale of
upper-body parts of people detected in stereo image pairs
from realistic stereo videos that can be found in the Internet.
To address this task, we propose a novel pictorial structure
model to exploit the stereo information included in such
stereo image pairs: the Stereo Pictorial Structure (SPS). To
validate our proposed model, we contribute a new annotated
dataset of stereo image pairs, the Stereo Human Pose Estimation
Dataset (SHPED), obtained from YouTube stereoscopic
video sequences, depicting people in challenging poses
and diverse indoor and outdoor scenarios. The experimental
results on SHPED indicates that SPS improves on state-ofthe-
art monocular models thanks to the appropriate use of
the stereo informatio
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