43 research outputs found
CLOTH3D: Clothed 3D Humans
This work presents CLOTH3D, the first big scale synthetic dataset of 3D
clothed human sequences. CLOTH3D contains a large variability on garment type,
topology, shape, size, tightness and fabric. Clothes are simulated on top of
thousands of different pose sequences and body shapes, generating realistic
cloth dynamics. We provide the dataset with a generative model for cloth
generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on
graph convolutions (GCVAE) to learn garment latent spaces. This allows for
realistic generation of 3D garments on top of SMPL model for any pose and
shape
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
Creatures Great and SMAL: Recovering the Shape and Motion of Animals from Video
We present a system to recover the 3D shape and motion of a wide variety of
quadrupeds from video. The system comprises a machine learning front-end which
predicts candidate 2D joint positions, a discrete optimization which finds
kinematically plausible joint correspondences, and an energy minimization stage
which fits a detailed 3D model to the image. In order to overcome the limited
availability of motion capture training data from animals, and the difficulty
of generating realistic synthetic training images, the system is designed to
work on silhouette data. The joint candidate predictor is trained on
synthetically generated silhouette images, and at test time, deep learning
methods or standard video segmentation tools are used to extract silhouettes
from real data. The system is tested on animal videos from several species, and
shows accurate reconstructions of 3D shape and pose.GlaxoSmithKlin
End-to-end 6-DoF Object Pose Estimation through Differentiable Rasterization
Here we introduce an approximated differentiable renderer to refine a 6-DoF pose prediction using only 2D alignment information. To this end, a two-branched convolutional encoder network is employed to jointly estimate the object class and its 6-DoF pose in the scene. We then propose a new formulation of an approximated differentiable renderer to re-project the 3D object on the image according to its predicted pose; in this way the alignment error between the observed and the re-projected object silhouette can be measured. Since the renderer is differentiable, it is possible to back-propagate through it to correct the estimated pose at test time in an online learning fashion. Eventually we show how to leverage the classification branch to profitably re-project a representative model of the predicted class (i.e. a medoid) instead. Each object in the scene is processed independently and novel viewpoints in which both objects arrangement and mutual pose are preserved can be rendered.
Differentiable renderer code is available at:https://github.com/ndrplz/tensorflow-mesh-renderer
RNA-sequencing elucidates the regulation of behavioural transitions associated with mating in honey bee queens
This study was funded by a BBSRC ISIS grant BB/J019453/1, a Royal Holloway Research Strategy Fund Grant, and a Leverhulme Grant F/07537/AK to MJFB. BPO was supported by Australian Research Council Discovery grants DP150100151 and DP120101915. FM was supported by a Marie Curie International Incoming Fellowship FP7-PEOPLE-2013-IIF-625487 to MJFB. We would like to thank Dave Galbraight (Penn State) and Alberto Paccanaro (RHUL) for support with analysis of RNAseq data and four anonymous reviewers for providing thoughtful insights that helped to improve the manuscript.Peer reviewedPublisher PD
Parametric Model-Based 3D Human Shape and Pose Estimation from Multiple Views
Human body pose and shape estimation is an important and challenging task in computer vision. This paper presents a novel method for estimating 3D human body pose and shape from several RGB images, using detected joint positions in the images and based on a parametric human body model. Firstly, the 2D joint points of the RGB images are estimated using a deep neural network, which provides a strong prior on the pose. Then, an energy function is constructed based on the 2D joint points in the RGB images and a parametric human body model. By minimizing the energy function, the pose, shape and camera parameters are obtained. The main contribution of the method over previous work, is that the optimization is based on several images simultaneously using only estimated joint positions in the images. We have performed experiments on both synthetic and real image data-sets, that demonstrate that our method can reconstruct 3D human bodies with better accuracy than previous single view methods