530 research outputs found
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We describe the first method to automatically estimate the 3D pose of the
human body as well as its 3D shape from a single unconstrained image. We
estimate a full 3D mesh and show that 2D joints alone carry a surprising amount
of information about body shape. The problem is challenging because of the
complexity of the human body, articulation, occlusion, clothing, lighting, and
the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a
recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D
body joint locations. We then fit (top-down) a recently published statistical
body shape model, called SMPL, to the 2D joints. We do so by minimizing an
objective function that penalizes the error between the projected 3D model
joints and detected 2D joints. Because SMPL captures correlations in human
shape across the population, we are able to robustly fit it to very little
data. We further leverage the 3D model to prevent solutions that cause
interpenetration. We evaluate our method, SMPLify, on the Leeds Sports,
HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect
to the state of the art.Comment: To appear in ECCV 201
Inner Space Preserving Generative Pose Machine
Image-based generative methods, such as generative adversarial networks
(GANs) have already been able to generate realistic images with much context
control, specially when they are conditioned. However, most successful
frameworks share a common procedure which performs an image-to-image
translation with pose of figures in the image untouched. When the objective is
reposing a figure in an image while preserving the rest of the image, the
state-of-the-art mainly assumes a single rigid body with simple background and
limited pose shift, which can hardly be extended to the images under normal
settings. In this paper, we introduce an image "inner space" preserving model
that assigns an interpretable low-dimensional pose descriptor (LDPD) to an
articulated figure in the image. Figure reposing is then generated by passing
the LDPD and the original image through multi-stage augmented hourglass
networks in a conditional GAN structure, called inner space preserving
generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human
figures, which are highly articulated with versatile variations. Test of a
state-of-the-art pose estimator on our reposed dataset gave an accuracy over
80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to
preserve the background with high accuracy while reasonably recovering the area
blocked by the figure to be reposed.Comment: http://www.northeastern.edu/ostadabbas/2018/07/23/inner-space-preserving-generative-pose-machine
Some closure operations in Zariski-Riemann spaces of valuation domains: a survey
In this survey we present several results concerning various topologies that
were introduced in recent years on spaces of valuation domains
Learning 3D Human Pose from Structure and Motion
3D human pose estimation from a single image is a challenging problem,
especially for in-the-wild settings due to the lack of 3D annotated data. We
propose two anatomically inspired loss functions and use them with a
weakly-supervised learning framework to jointly learn from large-scale
in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal
network that exploits temporal and structural cues present in predicted pose
sequences to temporally harmonize the pose estimations. We carefully analyze
the proposed contributions through loss surface visualizations and sensitivity
analysis to facilitate deeper understanding of their working mechanism. Our
complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M
and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics
card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose
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Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
Optical marker-based motion capture is a vital tool in applications such as
motion and behavioural analysis, animation, and biomechanics. Labelling, that
is, assigning optical markers to the pre-defined positions on the body is a
time consuming and labour intensive postprocessing part of current motion
capture pipelines. The problem can be considered as a ranking process in which
markers shuffled by an unknown permutation matrix are sorted to recover the
correct order. In this paper, we present a framework for automatic marker
labelling which first estimates a permutation matrix for each individual frame
using a differentiable permutation learning model and then utilizes temporal
consistency to identify and correct remaining labelling errors. Experiments
conducted on the test data show the effectiveness of our framework
Good Friends, Bad News - Affect and Virality in Twitter
The link between affect, defined as the capacity for sentimental arousal on
the part of a message, and virality, defined as the probability that it be sent
along, is of significant theoretical and practical importance, e.g. for viral
marketing. A quantitative study of emailing of articles from the NY Times finds
a strong link between positive affect and virality, and, based on psychological
theories it is concluded that this relation is universally valid. The
conclusion appears to be in contrast with classic theory of diffusion in news
media emphasizing negative affect as promoting propagation. In this paper we
explore the apparent paradox in a quantitative analysis of information
diffusion on Twitter. Twitter is interesting in this context as it has been
shown to present both the characteristics social and news media. The basic
measure of virality in Twitter is the probability of retweet. Twitter is
different from email in that retweeting does not depend on pre-existing social
relations, but often occur among strangers, thus in this respect Twitter may be
more similar to traditional news media. We therefore hypothesize that negative
news content is more likely to be retweeted, while for non-news tweets positive
sentiments support virality. To test the hypothesis we analyze three corpora: A
complete sample of tweets about the COP15 climate summit, a random sample of
tweets, and a general text corpus including news. The latter allows us to train
a classifier that can distinguish tweets that carry news and non-news
information. We present evidence that negative sentiment enhances virality in
the news segment, but not in the non-news segment. We conclude that the
relation between affect and virality is more complex than expected based on the
findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet
talk your friends or serve bad news to the public'.Comment: 14 pages, 1 table. Submitted to The 2011 International Workshop on
Social Computing, Network, and Services (SocialComNet 2011
Model-free Consensus Maximization for Non-Rigid Shapes
Many computer vision methods use consensus maximization to relate
measurements containing outliers with the correct transformation model. In the
context of rigid shapes, this is typically done using Random Sampling and
Consensus (RANSAC) by estimating an analytical model that agrees with the
largest number of measurements (inliers). However, small parameter models may
not be always available. In this paper, we formulate the model-free consensus
maximization as an Integer Program in a graph using `rules' on measurements. We
then provide a method to solve it optimally using the Branch and Bound (BnB)
paradigm. We focus its application on non-rigid shapes, where we apply the
method to remove outlier 3D correspondences and achieve performance superior to
the state of the art. Our method works with outlier ratio as high as 80\%. We
further derive a similar formulation for 3D template to image matching,
achieving similar or better performance compared to the state of the art.Comment: ECCV1
General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues
Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way
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