text:
\section{Conclusion}

We presented a deep learning technique for estimating 3D human body shape and pose from a single color image. To that end, we propose an iterative training approach that alternates between deformable surface registration and training of deep ConvNets, which gradually improves accuracy of predictions by extracting and aggregating 3D information from dense correspondences provided on 2D images. This approach allows us to learn 3D body shapes and pose from 2D dataset only without having to use 3D annotations that are in general very expensive to obtain. More importantly, as our approach does not rely on statistical body models or 3D annotations  captured in a controlled environment, our method is not restricted to the space of the pre-built statistical model and can capture body shape and pose details contained in in-the-wild images better than previous approaches.

In future work, we would like to address the modeling of clothing and details. We are interested in designing a single unified network that can handle from 2D detection to 3D body shape/pose prediction all at once, which would be more efficient and faster. It would also be beneficial to find a better representation of 3D human body and pose than  body shape/pose parametric representation, which is more friendly to be used by ConvNets.

\section{Introduction}

Estimating 3D human pose and body shape from a single image is a challenging yet important problem, with a wide variety of applications such as computer animation and virtual try-on in fashion.

Capturing and modeling of 3D body shape and pose has been mostly done in controlled settings using specialized 3D scanners such as a whole-body laser range scanner and motion capture (MoCap) system. With the progress of deep convolutional neural networks (ConvNets), 3D body shape can be estimated from a single image by regressing the parameters of statistical human body models. Most of current methods rely on 3D database for both body shape and pose, which still requires expensive 3D scanning systems to construct and extend their dataset that they use for training.

Nonetheless, the capability of the current methods for expressing body shape and pose is rather limited because of two main reasons. Firstly, regression of body shape parameters is inherently difficult for deep ConvNets. The mapping between the input image and parameters of statistical body models is highly nonlinear and is currently difficult to learn. The second challenge is the lack of a large-scale 3D dataset. In fact, most of 3D body shape dataset are limited to the age range from young adult to middle age. Also, MoCap dataset for 3D human pose estimation are limited to a small variety of subjects, since it needs a complicated experimental setup where MoCap and RGB video cameras have to be synchronized. Because those motion data are acquired in a controlled environment, they are somewhat different from the natural poses that can be found in the in-the-wild images.

{\it ``Can we learn 3D human body shape and pose directly from 2D images?''} In this paper, we tackle this challenging problem to bypass the 3D dataset scarcity problem by extracting and aggregating 3D information from dense correspondences annotated on 2D images. We propose a strategy called ``deform-and-learn" where we alternates deformable surface registration that fits a 3D model to  2D images and training of deep neural network that predicts 3D body shape and pose from a single image. Given dense image-to-surface  correspondences, the first registration step fits a template model to images. The result is then used as supervisional signals of 3D body shape and pose for training deep ConvNets in the second registration step. These two processes are iterated to improve accuracy. Unlike previous approaches, our method does not require statistical body shape models, 3D pose annotations from MoCap dataset or human interventions to validate 3D pose.

The contributions of this paper are summarized as follows:

\begin{itemize}
	\item
	We propose a deform-and-learn training strategy that alternates deformable registration and training of a deep ConvNets for estimating human body shape and pose. It uses dense correspondences annotated on 2D images, without having to use 3D dataset (body shape and pose).

	\item
	To design a pose prior from 2D pose dataset, we propose a conditional generative adversarial networks (cGANs) for detecting 3D human joint positions from 2D keypoints. We incorporate geometric constraints in cGANs to further constrain 3D human pose predictions. The results are used as soft constraints to guide the training of deep ConvNets for body shape and pose estimation.

	\item
	We propose a skeleton-based deformable registration technique using back propagation, which can be implemented using a deep learning framework and parallelized with GPU. With the autograd technique, adding and minimization of various types of losses can be made simple, which frees our method from relying on 3D dataset and pre-built 3D statistical models.

	\item
	We propose a deep ConvNets that predicts body shape and pose using scalings of body segments as   body shape representation. With the final refinement step based on deformable registration using dense correspondence predictions, we can further align a body mesh model to an image.

\end{itemize}

\begin{figure*}[h]
	\centering
	\includegraphics[width=1\linewidth]{images/concept.jpg}
	\caption{Overview of our deform-and-learn training strategy that iteratively performs deformable registration and deep learning. Let $\mathbf{\theta}$ be the parameters of the body model, such as body shape and pose. In the very beginning, the initial pose of registration is in the T-pose, ${\bf \theta}_0$. Given dense image-to-surface  correspondences, the first registration step fits a template model to images. After registration, we obtain a collection of ${\bf \theta}_{\rm fit}$ which is then used as supervisional signals ${\bf \theta}_{\rm anno}$ to train deep ConvNets that predicts body parameters ${\bf \theta}_{\rm conv}$. The results of ConvNets are used as  initial poses of deformable registration in the next round. These two steps are iterated to get better results.}
	\label{fig:overview}
\end{figure*}

\section{Conditional generative adversarial networks for 3D human pose with geometric constraints}
\label{sec:cgan}

We propose a conditional generative adversarial networks (GANs) to predict depths of joints from 2D keypoints in an unsupervised manner. The results of the generator is used as soft constraints to guide image-surface registration in the next section.

We take a similar approach as Kudo et al. \cite{kudo2018} and Chen et al. \cite{Chen2019} where the 3D joint positions produced by a generator network ($G$) is projected to the image plane to obtain 2D joint positions and a discriminator ($D$) judges real or fake in 2D image space. The key difference of our model from previous approaches \cite{kudo2018} is that our approach incorporates geometric constraints, such as bone symmetry constraints, to further constrain the space of solution. The network architecture is depicted in Fig. \ref{fig:GAN}. The input to the generator is the 2D key points of $N$ joints and the output is depths of those joints. The predicted depths values $z_i$ are then  concatenated with $x_i$ and $y_i$ coordinates, rotated around the vertical axis and projected to the image space. The discriminator inputs the projected joint positions as $fake$ and the 2D keypoint data as $real$. For both networks, we use multi-layer perceptron (MLP) with eight linear layers to map 2D coordinates to depths and binary class.

Let ${\bf u}$ be the 2D joint positions of a skeleton. Also let us denote an angle around the vertical axis as $\phi$. Our 3D human pose cGANs uses the following standard adversarial loss functions for $G$ and $D$:
\begin{align}
{\cal L}_{\rm adv}^G &= E_{{\bf u}, \phi} [\log (1- D (f({\bf u}, G({\bf u}); \phi)))  \nonumber \\ {\cal L}_{\rm adv}^D &= E[\log D({\bf u})] \nonumber
\end{align}
where $f$ denotes the rotation and the projection function. Note that we validate the pose from multiple views where we used angles [deg], $\phi =  \{45, 60, 90, 135, 180, 235, 270 \}$ for each pose.

In addition to the adversarial loss, the geometric loss is also applied. Specifically, we use the bone symmetry loss ${\cal L}_{\rm sym}$ that constrain the left and right limb be similar and the bone ratio loss ${\cal L}_{\rm ratio}$ that minimizes the difference between the normalized bone length prediction and that of dataset. The bone ratio loss ${\cal L}_{\rm ratio}$ is defined as:
\begin{equation}
{\cal L}_{\rm ratio} = \sum_{e \in{\cal B}} \|\frac{l_e}{l_{\rm trunk}} - \frac{\bar{l}_e}{\bar{l}_{\rm trunk}}   \|^2 \nonumber
\end{equation}
where $\frac{l_e}{l_{\rm trunk}}$ is the ratio of the bone length for bone $e$ in a set of bones  ${\cal B}$ in a skeleton   with respect to the trunk length and $\frac{\bar{l}_e}{\bar{l}_{\rm trunk}}$ is that of the average skeleton.   Let ${\cal B}_s$ be the set of symmetry pairs of bone segments which contains indices of bones e.g., the left and right forearm. Then the bone symmetry loss ${\cal L}_{\rm sym}$ is defined as:
\begin{equation}
{\cal L}_{\rm sym} = \sum_{i,j \in{\cal B}_s } \|l_i- l_j \|^2 \nonumber\\
\end{equation}
where $l_i$ and $l_j$ is the lengths of the bone for symmetry bone pairs. We mix the above losses to train the generator such that the loss is:
\begin{equation}
{\cal L}^G = \epsilon{\cal L}_{\rm adv}^G + ({\cal L}_{\rm ratio} + {\cal L}_{\rm sym}) \nonumber\\
\end{equation}
where $\epsilon$ is the weight for controlling the strength of the adversarial term, which we set to 0.1 in this paper.

\begin{comment}
\section*{Appendix B: Deriving depth ordering from dense correspondences }
To define the depth ordering loss, for each image we extract ordinal depth pairs of vertices, one of which is an index of the closet vertex to the camera and the other is the vertex index that is located at the same location in the image space but far apart in the depth direction. Since we have established correspondences between surface and image points from DensePose annotations, the closest point to the camera is already identified. To find the vertices that is located distant from the camera in visibility line, we deform a template mesh by Laplacian deformation to fit to the dense annotations. As shown in Fig. the depths is smoothed out by this operation. From dense annotations, we search from nearest neighbor points of the deformed mesh. If the segment index is different than , we assigned it as 'far'.
\end{comment}

\section{Image-surface deformable registration}
\label{sec:regist}
We propose a deformable surface registration technique to fit a template mesh model to images to obtain 3D body shape and pose annotations for training deep ConvNets. Here deformable  registration is formulated as a gradient-based method based on back propagations, which can be implemented with a deep learning framework and parallelized with GPU. With the automatic differentiation mechanisms provided with a deep learning framework, adding and minimizing various kinds of losses have made easy and straightforward. As a result, the proposed deformable registration technique thus incorporates kinematic, geometric and correspondence losses.

Given image-surface dense correspondences annotated on images, the template mesh is fitted to images by optimizing body parameters ${\bf \theta} = [{\bf a}, {\bf S},{\bf R}, s, {\bf t}]$ subject to kinematic and geometric constraints. In total, the overall loss function for our  registration is of the form:
\begin{align}
{\cal L}_{\rm regist}  &= \omega_{\rm dense} {\cal L}_{\rm dense} + \omega_{\rm KP} {\cal L}_{\rm KP} \\ \nonumber &+ \omega_{\rm scale} {\cal L}_{\rm scale} + \omega_{\rm joint} {\cal L}_{\rm joint} \nonumber + \omega_{\rm det} {\cal L}_{\rm det} \nonumber
\end{align}
where ${\cal L}_{\rm dense}$ and ${\cal L}_{\rm KP}$ are the dense correspondence and key point losses that penalize the alignment inconsistency of the body model and images defined in terms of dense correspondences and key points. The losses ${\cal L}_{\rm  scale}$ and ${\cal L}_{\rm  joint}$ is the segment scaling smoothness and kinematic loss for regularization. The transformation determinant loss ${\cal L}_{\rm det}$ makes the determinant of the global transformation positive. In addition,  $\omega_{\rm dense}$,  $\omega_{\rm KP}$, $\omega_{\rm scale}$, $\omega_{\rm joint}$ and $\omega_{\rm det}$ are the respective weights for the above defined losses. The initialization of body parameters is provided from the predictions of deep ConvNets. For the very first iteration where the Convnet predictions are not available, segment scale ${\bf S}$ is set 1 for all segments and pose ${\bf a}$ is set to 0 for all joints, which means that registration is started from the T pose.

\subsection{Correspondence fit loss}
\label{sec:corresp}

The correspondence loss comprises two losses: the dense correspondence loss ${\cal L}_{\rm Dense}$ and keypoint loss ${\cal L}_{\rm KP}$.

\noindent {\bf Dense correspondence loss } Let us define a set of image-surface correspondences $\mathcal{C}=\{(\mathbf{p}_{1}, \mathbf{v}_{\mathrm{idx}(1)}) \ldots (\mathbf{p}_{N}, \mathbf{v}_{\mathrm{idx}(N)}) \}$, where $\mathbf{p}$ is the image points. In addition $\mathrm{idx}(i)$ is the index of the mesh vertices that is matched with image point $i$. Now we can define the dense correspondence loss as:
\begin{eqnarray}
\label{eq:Ec}
{\cal L}_{\rm dense} = \sum_{i\in \mathcal{C}} \|{\bf p}_{i} - \mathbf{x}_{\mathrm{idx}(i)} \|^2 \nonumber
\end{eqnarray}
where a mean squared error (MSE) is used to calculate the loss.

\begin{comment}

\noindent {\bf Depth ordering loss } From DensePose annotations, the visibility information in depth direction can also be extracted. For example, the parts that are annotated in the image have to be the closest in depth direction. Following the ordinal depth supervision approach, we adopted the ordinal ranking loss. Given the surface point pair $(i,j)$ and assuming that the ConvNet is producing the depth estimates $d_i$ and $d_j$ for the two corresponding surface points, the loss is:

\begin{eqnarray}
{\cal L}_{\rm DOrder} =  \sum_{i, j \in \mathcal{P}} {\rm log} (1 + {\rm exp}(d_{i} - d_{j} )) \nonumber
\end{eqnarray}

This is a differentiable ranking loss expression, which has similarities with early works on the learning-to-rank literature for e.g. apparent depth estimation. Intuitively, it enforces a large margin between the values $d_i$ and $d_j$. \\
\end{comment}

\noindent {\bf Key point loss } To produce 3D poses with statistically valid depths, the results of cGAN is used to guide deformable registration. Instead of attaching a discriminator to the registration framework, the depth values from cGAN and the ground truth 2D joint coordinates are provided as a soft constraint to constrain the position of the 3D joints based on the MSE loss:
\begin{eqnarray}
{\cal L}_{\rm KP} = \sum_{i\in \mathcal{J}} \|x_i - \bar{x}_i \|^2
+ \sum_{i\in \mathcal{J}} \|y_i - \bar{y}_i \|^2
+ \sum_{i\in \mathcal{J}} \|z_i - z_i^{\rm GAN} \|^2 \nonumber
\end{eqnarray}
where $\bar{x}_i$ and $\bar{y}_i$ are the ground truth of 2D key points. Also $z_i^{\rm GAN}$ is the depth at joint $i$ predicted by cGANs.

\subsection{Geometric and kinematic loss}
Since we attract the template mesh to 2D image coordinates, the problem is ill-posed and deformations are not constrained. Thus we introduce the regularization terms that avoids extreme deformations.

\noindent {\bf Segment scaling smoothness } To avoid extreme segment scalings, we introduce the scaling smoothness loss, which minimizes difference between scalings of adjacent segments:
\begin{equation}
{\cal L}_{\rm scale} = \sum_{e \in \mathcal{B}} \|{\bf S}_e - {\bf S}_{{\rm adj}(e)}\|^2 \nonumber
\end{equation}
\noindent {\bf Joint angle smoothness and limit loss } To prevent extreme poses, we introduce joint angle smoothness loss and joint limit loss. The joint smoothness loss is enforced at every joint in a skeleton, $\mathcal{J}$, and will contribute to avoid extreme bending. To avoid hyper-extensions which will bend certain joints like the elbows and knees (where we represent as $\mathcal{J'}$) in the negative direction, we introduce the joint limit loss. The regularizations that act on joints are thus represented as:
\begin{equation}
{\cal L}_{\rm joint} =  \sum_{i \in \mathcal{J}} \|	{\bf a}_i\|^2 + \sum_{i \in \mathcal{J'}} \|{\rm exp}({\bf a}_i)\|^2 \nonumber
\end{equation}
where the first term minimizes joint angles whereas the latter term penalizes rotations violating natural constraints by taking exponential and minimizing it.

\noindent {\bf Transformation determinant loss } Since we use a rotation matrix for representing the global rotation at the root, it is necessary to apply a constraint on a matrix to keep its determinant to positive. Thus, we define the transformation determinant loss as:
\begin{eqnarray}
{\cal L}_{\rm det} =  \exp (-{\rm det}({\bf R})) \nonumber
\end{eqnarray}

\begin{figure*}[t]
	\centering
	\includegraphics[width=1\linewidth]{images/refinement.jpg}
	\caption{Refinement based on densepose prediction. The result of body parameter regression networks is refined based on a deformable surface registration technique. To define the dense correspondence term, we converted to image-surface correspondences obtained from DensePose predictions of uv maps and part indices. }
	\label{fig:refine_method}
\end{figure*}

\section{Estimating 3D human body shape and pose from a single image}
\label{sec:conv}
\subsection{Deep ConvNets for body shape and pose regression}

Using the results obtained by deformable registration as annotations for training deep ConvNets, we regress body shape and pose parameters with an image. We also add the dense correspondence and keypoint losses as in Section \ref{sec:corresp} for additional supervisions. In total, we minimize the loss function of the form:
\begin{equation}
{\cal L}_{\rm conv} =  \alpha {\cal L}_{\rm regress} +   \beta {\cal L}_{\rm dense} + \gamma {\cal L}_{\rm KP}  \nonumber
\end{equation}
where ${\cal L}_{\rm regress}$ is the regression loss for body parameters. $\alpha$, $\beta$ and $ \gamma$  are the respective weights. Let $\theta_i$ be the parameters for $i$-th sample, the regression loss is defined as:
\begin{equation}
{\cal L}_{\rm regress} = \sum_{i} \; {\rm smooth}_{L1} (\theta_i - {\theta}_i^{\rm anno} )  \nonumber
\end{equation}
where  ${\theta}_i^{\rm anno}$ is the annotation provided from the registration step. Here we use the smooth L1 loss because of its robustness to outliers. This choice was more effective than the L2 loss in contributing to decreasing the error during the iterative training strategy in the presence of potential outliers and noisy annotations.

The body model is similar to the one we used for registration, except for  the pose representation, where we found that the use of quaternions improved stability and convergence of training than axis angle, which is probably due to the fact that the values of quaternions are in between -1 and 1 and is easier for ConvNets to learn with than axis angles.  Other parameters are same as the ones used in Section \ref{sec:regist}, which results in 132 parameters in total. Note that the global rotation is regressed using 9 parameters and the Gram Schmidt orthogonalization is used to make a transformation into a rotation. We use ResNet50 \cite{DBLP:journals/corr/HeZRS15} pretrained on the ImageNet dataset as the base network.

\subsection{Inference and final refinement based on registration}

During the inference phase, there are two steps: 3D body parameter prediction and skeleton-based deformation. Since body shape/pose parameters are highly non-linear and are difficult to regress and predict accurately using deep ConvNets, we optionally provide a way to refine ConvNet predictions. This is based on the deformable registration technique proposed in Section \ref{sec:regist}. In order to define the dense correspondence term, we use DensePose \cite{Guler2018DensePose} to obtain dense uv maps and part indices (Fig. \ref{fig:refine_method}), which are then converted to image-surface correspondences. In addition, the simple baseline 2D human pose detector \cite{xiao2018simple} is used to obtain 2D human joint positions from an image and to define the key point loss. The pre-trained models from \cite{Guler2018DensePose} and \cite{xiao2018simple} are used.

\begin{comment}
\subsubsection{Surface normal sign loss}
One way to enforce visibility consistency is to consider the direction or surface normals. In particular, the vertex that has a corresponding point on an image should be visible from the viewpoint. Thus, at each vertex that has correspondence to an image point, we compare transformed surface normals and a viewpoint vector and check the sign of those two vectors. As the viewpoint vector is $[0,0,1]$, the sign of $z$ components of the surface normals should be negative:
\begin{eqnarray}
{\cal L}_{\rm normal} = \sum_{i \in \cal{C}}  \|{\rm sign} (n_i^z) + 1 \|^2
\end{eqnarray}
\end{comment}
\begin{comment}
\noindent {\bf Conformal loss } Conformal mapping constrains a local transformation as $\mathbf{T}^\mathrm{T}\mathbf{T} = s^2 \mathbf{I_d}$. It must satisfy six conditions: all columns must have the same norm and must be orthogonal to one another.
\begin{eqnarray}
{\cal L}_{\rm conformal} = \sum_i \mathrm{ Conformal} (\mathbf{ T}_i)\nonumber
\end{eqnarray}
\begin{eqnarray}
\mathrm{ Conformal} (\mathbf{ T}) =& (\mathbf{ c}_1^\mathrm{ T} \mathbf{ c}_2)^2 + (\mathbf{ c}_2^\mathrm{ T} \mathbf{ c}_3)^2 + (\mathbf{ c}_3^\mathrm{ T} \mathbf{ c}_1)^2 \\ \nonumber
+& (\mathbf{ c}_1^\mathrm{ T} \mathbf{ c}_1 - \mathbf{ c}_2^\mathrm{ T} \mathbf{ c}_2)^2\\ \nonumber
+& (\mathbf{ c}_2^\mathrm{ T} \mathbf{ c}_2 - \mathbf{ c}_3^\mathrm{ T} \mathbf{ c}_3)^2\\ \nonumber
+& (\mathbf{ c}_3^\mathrm{ T} \mathbf{ c}_3 - \mathbf{ c}_1^\mathrm{ T} \mathbf{ c}_1)^2 \nonumber
\end{eqnarray}
where $\mathbf{ c}_1$, $\mathbf{ c}_2$ and $\mathbf{ c}_3$ are the $3 \times 1$ column vectors of $\mathbf{ T}$. We minimize this error using the mean squared error (MSE) loss where the target of each component is set to zero.    \\

\noindent {\bf Volume positivity loss } Previous methods often constrains volumes or scales of the model by employing equiareal or equivolume constraints on transformations, i.e. $\| {\rm det}({\bf T}) - 1 \|^2$. Here, to maximize the flexibility to change scales of the segments while avoiding volume sign changes, we minimize volume positivity loss:
\begin{eqnarray}
{\cal L}_{\rm volPlus} = \| {\rm sign}({\rm det}({\bf T})) - 1 \|^2
\end{eqnarray}

\noindent {\bf Transformation smoothness } The smoothness term serves as a regularizer for the deformation by indicating that the linear transformations of adjacent segments  should agree with one another:
\begin{eqnarray}
{\cal L}_{\rm TS}  = \sum_{i, j \in \mathcal{N}} \| \mathbf{T}_i - \mathbf{T}_j \|^2
\end{eqnarray}
where $\mathcal{N}$ is the set of adjacent segment pairs in a skeleton.   \\

\noindent {\bf Volume smoothness loss } We also impose a constraint to regularize the  changes in volumes of segments. This is done by taking the determinants of transformations and minimizing the difference between that of adjacent ones:
\begin{eqnarray}
{\cal L}_{\rm VS} = \sum_{i, j \in \mathcal{N}} \| {\rm det}({\bf T}_i) - {\rm det}({\bf T}_j) \|^2
\end{eqnarray}

\end{comment}

\begin{comment}
content...
The optimization consists of two loops: The outer loop searches for the nearest neighbor points and constructs the closest point term with $w_\mathrm{reg}$ set to some value. The inner loop then optimizes the affine transformations at the vertices with the fixed position constraints by minimizing $E(\mathbf{X})$. Once this is converged, $w_\mathrm{ reg}$ is halved and the outer loop finds the closest points again.

\textbf{Nonlinear least squares}
We minimize the nonlinear energy $E(\mathbf{X}) = w_\mathrm{ ACAP} E_\mathrm{ ACAP} + w_\mathrm{ reg} E_\mathrm{ reg} + w_\mathrm{ C} E_\mathrm{ C}+ w_\mathrm{F} E_\mathrm{ F}$ using an iterative Gauss-Newton method \cite{SSP06}. We unrolled $\mathbf{X}$ and define stacked variables by a $12 n \times 1$ column vector $\mathbf{x}$. The Gauss-Newton algorithm linearizes the nonlinear problem with Taylor expansion about $\mathbf{x}$:
\begin{eqnarray*}
\mathbf{ f}(\mathbf{x} + \mathbf{\delta}) = \mathbf{f}(\mathbf{x}) + \mathbf{ J}\mathbf{\delta}
\end{eqnarray*}
The vector $\mathbf{f}(\mathbf{x})$ stacks the squared roots of the cost functions, so that $\mathbf{f}(\mathbf{x})^\mathrm{T} \mathbf{f}(\mathbf{x}) = E(\mathbf{x}) = w_\mathrm{ ACAP} E_\mathrm{ ACAP} + w_\mathrm{reg} E_\mathrm{reg} + w_\mathrm{ C} E_\mathrm{ C}+ w_\mathrm{ F} E_\mathrm{ F}$.
$\mathbf{ J}$ is the Jacobian matrix of $\mathbf{f}(\mathbf{x})$. At each iteration $t$, we solve a linearized problem and compute an updating vector $\mathbf{ \delta}_t$ to improve the current solution $\mathbf{x}_{t}$:
\begin{eqnarray}
\label{eq:GN}
\mathbf{ J}_t^\mathrm{ T} \mathbf{ J}_t\mathbf{ \delta}_t = - \mathbf{ J}_t^\mathrm{ T} \mathbf{ f}(\mathbf{x}_t)\\ \nonumber
\mathbf{x}_{t+1} = \mathbf{x}_{t} + \mathbf{\delta}_t
\end{eqnarray}
In each Gauss-Newton iteration, we solve the normal equations by Cholesky factorization. We must calculate both the symbolic and numeric factorization of $\mathbf{J}_t$ once after the outer loop finds the closest points. In the inner loop, however, the non-zero structure of $\mathbf{J}_t$ remains unchanged. Thus, we can reuse the symbolic factorization to speed up computations. The inner loop typically takes 6 iterations until convergence.
\end{comment}
\section{Problem formulation}

The goal of our work is to learn a model that can predict 3D body shape and pose from a single image using deep ConvNets, without having to use 3D dataset. To the best of our knowledge, this paper is the first one to achieve it. To that end, we use dense correspondence annotations (Fig. \ref{fig:dennse_annotation}) between image points and a body surface, which can be annotated on 2D images in-the-wild and provides rich information about body shape and pose. Compared to silhouettes and part segmentation, dense correspondence annotations are less noisy around boundaries and can be obtained with some more additional human efforts whose annotation time is almost the same as that of part segmentation  \cite{Guler2018DensePose}.

Although dense correspondences between a body surface and image points contain rich information, they themselves are not sufficient for recovering 3D body shape and pose, especially for depth. The strategy we take in this paper is to incorporate geometric and kinematic losses imposed on body parameters as well as an adversarial loss defined from 2D key points to constrain the space of body shape and pose, as we do not have a direct access to 3D dataset that we can use for training. Consequently, the total loss we define and wish to minimize is as follows:
\begin{align}
{\cal L}  =  {a}{\cal L}_{\rm dense} + {b}{\cal L}_{\rm geo} + {c}{\cal L}_{\rm adv}
\label{eq:loss}
\end{align}
where ${\cal L}_{\rm dense}$ is the dense correspondence loss which penalizes the inconsistency of fits between the body model and images defined in terms of dense correspondences, ${\cal L}_{\rm geo}$ is the geometric and kinematic loss for regularization and ${\cal L}_{\rm adv}$ is the adversarial loss to constrain the distribution of the predicted poses close to that of 2D keypoint annotations. The weights  $a$, $b$ and $c$ control the relative strengths of the terms.

\noindent {\bf Deform-and-learn iterative training strategy } Directly minimizing all of the losses in Eq. (\ref{eq:loss}) at the same time is difficult and in fact we experienced that the error  stayed high. Instead, we decouple the problem into three components: A) training of a conditional generative adversarial networks that predicts 3D joints from 2D keypoints; B) optimization of latent body parameters based on image-surface registration; C) learning of body parameters by providing latent supervisions obtained in step B. The first component is trained once and provides soft constraints of 3D joints in the second and third step. The second and third components are iterated for several times to improve the accuracy, which we refer to as the ``deform-and-learn'' training strategy.

We found that decoupling of the training phase into three steps and providing supervisions on latent variables works effective. In fact, recent approaches showed that providing supervisions on latent body parameters are effective in stabilizing and improving training \cite{NBF:3DV:2018}. Here we reconstruct latent variables from dense annotations by deformable surface registration. Note that deformable registration is an local optimizer and is sensitive to an initial solution. This is why we propose the iterative training strategy ``deform-and-learn'', where we alternate between deformable registration and learning. This strategy will gradually improve performance by updating the initial solution of the registration phase and then the latent supervisions in the learning phase.

\noindent {\bf Body shape and pose model } To fit a template mesh model to an image, we use a skeleton-based parametric deformable model which is a modified version of SMPL  \cite{bogo2016keep}. The template mesh consists of $n$ vertices, where the number of vertex $n$ is 6980 in this paper. The vertex positions of the template, $\mathbf{v}_1 \ldots \mathbf{v}_n$, are denoted by a $n \times 3$ vector, $\mathbf{v}= [\mathbf{v}_1 \ldots \mathbf{v}_n]^\mathrm{ T}$. The pose of the body is defined by a skeleton rig with 23 joints where the pose parameters ${\bf a} \in \mathbb{R}^{24 \times 3}$ is defined by the axis angle representation of the relative rotation between segments. The body model is posed by a joint parameters ${\bf a}$ via forward kinematics. Instead of using a low-dimensional shape space as in \cite{bogo2016keep}, which can be learned from thousands of registered 3D scans, we use segment scales to model a body shape, which is parametrized by segment scales ${\bf S} \in \mathbb{R}^{24}$. This way, body shape can be modeled more flexibly without the need to use 3D body scans---it does not have to be confined in the space of statistical models. Using linear blend skinning, the body deformation model is defined as a function ${\bf v} = X({\bf S}, {\bf a})$.

We use the weak-perspective camera model and solve for the global rotation ${\bf R} \in \mathbb{R}^{3 \times 3}$, translation  ${\bf t} \in \mathbb{R}^{2}$ and global scale $s \in \mathbb{R}$. Rather than using other rotational representation such as axis angle, we directly optimize for a rotation matrix with 9 parameters due to its property to represent orientations uniquely in 3D space.  Since this approach makes a transformation deviating from a rotation matrix, we applied the Gram Schmidt normalization to ortho-normalize the matrix. Thus the total number of the parameters representing human body is 108,  ${\bf \theta} = [{\bf a}, {\bf S},{\bf R}, s, {\bf t}]$. With the body parameters ${\bf \theta}$, deformation and projection of vertices ${\bf v} = X({\bf S}, {\bf a})$ into an image is achieved as:
\begin{align}
{\bf x} = s \Pi ({\bf R} X({\bf S}, {\bf a})) + {\bf t}  \nonumber
\end{align}
where $\Pi$ is an  orthogonal projection.

\section{Overview}
The overview of our approach is depicted in Fig. \ref{fig:overview}. We train a conditional generative adversarial networks (cGANs) that predicts 3D joint positions from 2D joint positions, which will guide the registration and training of deep ConvNets for body shape and pose (Section \ref{sec:cgan}). The deform-and-learn training strategy alternates deformable surface registration that fits a 3D model to 2D images and training of deep neural network that predicts 3D body shape and pose from a single image (Section \ref{sec:regist}). In the very beginning, the initial pose of registration is in the T-pose, ${\bf \theta}_0$. Given image-surface dense correspondences, the first registration step fits a template model to images. After registration, we obtain a collection of body parameters ${\bf \theta}_{\rm fit}$ which is then used as supervisional signals ${\bf \theta}_{\rm anno}$ in order to train deep ConvNets that predicts body parameters ${\bf \theta}_{\rm conv}$ (Section \ref{sec:conv}). The results are used as  initial poses of surface registration in the next round. This training process is iterated for several times to get better results.

In the inference phase, we optionally perform refinement based on deformable surface registration. We first use the trained deep ConvNets to predict body shape and pose parameters, we refine the result using the registration technique starting from the ConvNet result as an initial solution. Thus the overall component used here is the same as the training phase, except that the order is flipped.

\begin{comment}

\begin{table}[t]
	{\footnotesize
		\begin{center}
			\caption[Comparison of mappings]{Comparison of mappings. $\mathbf{T}$ is a $3 \times 3$ linear transformation matrix. $\mathbf{C}$ is a stretch matrix. $\mathbf{I_d}$ is an identity matrix. $s$ is a scale. $\mathbf{R}$ is a rotation matrix. }
			\label{tab:Comp_mapping}
			\begin{tabular}{cccc}
				\hline
				& Property & $\mathbf{C} = \mathbf{T}^\mathrm{T}\mathbf{T}$ & $\mathbf{T}$  \\
				\hline
				Isometric & length-preserving & $\mathbf{I_d}$ & $\mathbf{R}$ \\
				Conformal & angle-preserving &  $s^2 \mathbf{I_d}$ & $s \mathbf{R}$ \\
				Equiareal & scale-preserving &  & $\mathrm{det}(\mathbf{T}) = 1$ \\
				Harmonic & smooth deformation &  & $ \mathrm{min} \| \mathbf{T} \|^2_F$\\
				\hline
			\end{tabular}
		\end{center}
	}
	\vspace{10pt}
\end{table}

\section{Background}
\label{sec:related}
\subsection{Classes of mappings}
Here, we briefly review the classes of mappings \cite{FH05} as a guide to choose an appropriate mapping for nonrigid surface registration. Suppose that $S$ is a surface and $f$ is a mapping from $S$ to a second surface $\tilde{S}$. We consider a 3D-to-3D mapping case, where a point at position $\mathbf{S}$ is deformed to $\tilde{\mathbf{S}}$ by a nonlinear function $f$,  $\tilde{\mathbf{S}} = f(\mathbf{S})$. We define an orthogonal local frame $\mathrm{d}\mathbf{S} = [\mathrm{d}\mathbf{S}_1 , \mathrm{d}\mathbf{S}_2 ,\mathrm{d}\mathbf{S}_3 ]$ at $\mathbf{S}$, which is deformed to $\mathrm{d}\tilde{\mathbf{S}} = [ \mathrm{d}\tilde{\mathbf{S}}_1 , \mathrm{d}\tilde{\mathbf{S}}_2 ,\mathrm{d}\tilde{\mathbf{S}}_3 ]$. A $3 \times 3$ local linear transformation $\mathbf{T}$ called the deformation gradient is calculated as $\mathbf{T} = \mathrm{d}\tilde{\mathbf{S}} \cdot (\mathrm{d}\mathbf{S})^{-1}$. The rotation-invariant measure of deformation,  the  Cauchy-Green stretch tensor, is defined as $\mathbf{C} = \mathbf{T}
^\mathrm{T}
\mathbf{T}$, which is an analogue of the first fundamental form. The properties of mappings are described as follows (Table \ref{tab:Comp_mapping}).

\textbf{ Isometric mappings }
A mapping from $S$ to $\tilde{S}$ is isometric or length-preserving if the length of any arc on $\tilde{S}$ is the same as that of the corresponding arc on $S$. When a mapping is isometric, $\mathbf{C}$ is an identity matrix, $\mathbf{C} = \mathbf{I_d}$. In other words, deformation is locally rigid (rotation), $\mathbf{T}= \mathbf{R}$.

\textbf{Conformal mappings }
A mapping from $S$ to $\tilde{S}$ is conformal or angle-preserving if the angle of intersection of every pair of the intersecting arcs on $\tilde{S}$ is the same as that of the corresponding arcs on $S$. When a mapping is conformal, the axes of the local frame must be orthogonal and have the same norm. In terms of stretch, it must satisfy $\mathbf{C} = s^2 \mathbf{I_d}$, where $s$ is a scale. In other words, a local transformation is scale and rotation $\mathbf{T} = s \mathbf{R}$; i.e., it is a similarity transformation. Thus, a circle and a sphere are transformed to a circle and a sphere, but their radii are allowed to change from their original values.

\textbf{Equiareal mappings }
A mapping from $S$ to $\tilde{S}$ is equiareal if every part of $S$ is mapped onto a part of $\tilde{S}$ with the same area.  It is scale preserving.

\textbf{ Harmonic mappings }
A mapping from $S$ to $\tilde{S}$ is harmonic if the deformation minimizes the Dirichlet energy:
{
	\abovedisplayskip=5pt
	\belowdisplayskip=5pt
	\begin{eqnarray*}
		E_D(f) = \frac{1}{2} \|\mathrm{grad}_\mathcal{S} \; f\|^2  \nonumber
	\end{eqnarray*}
}
where $\mathrm{grad}_\mathcal{S}$ is the gradient of the surface. Let $\mathbf{f}$ be a vector of a function defined on a surface. The solution for the minimization problem is obtained by solving the Laplace equation with some boundary constraints:
{
	\abovedisplayskip=5pt
	\belowdisplayskip=5pt
	\begin{eqnarray*}
		\Delta_\mathcal{S} \mathbf{f} = \mathbf{0} \;
	\end{eqnarray*}
}
where $\Delta_\mathcal{S}$ is the Laplace-Beltrami operator. The consequence of this minimization is that the boundary conditions are smoothly interpolated. When the mappings are harmonic, because the gradient of a mapping is equivalent to the deformation gradient, we are minimizing a local transformation, $ \mathrm{min} \| \mathbf{T} \|^2_F$.

There are two important implications that describe relationships between the above mappings. First, every isometric mapping is conformal and equiareal (scale-preserving):
{
	\abovedisplayskip=5pt
	\belowdisplayskip=5pt
	\begin{eqnarray*}
		\mathrm{isometric = conformal \cap equiareal} \nonumber
	\end{eqnarray*}
}
Second, a conformal mapping is the subspace of harmonic mapping:
{
	\abovedisplayskip=5pt
	\belowdisplayskip=5pt
	\begin{eqnarray*}
		\mathrm{conformal \subset harmonic} \nonumber
	\end{eqnarray*}
}
Thus, conformal mappings are always harmonic but the inverse is not true. Not all harmonic mappings are angle preserving.

\section{Overview}

\end{comment}

\section{Related Work}

\noindent {\bf Human body shape modeling and surface registration } Previously, modeling of 3D body shape is done with 3D scanners. The first approach in this line of work is done by Allen et al. \cite{ACP03} where the authors fit a template 3D body model to Caesar dataset that contains a couple of thousand subjects and used principal component analysis (PCA) to model the space of human body shape. Later, several techniques are proposed to extend the method of Allen et al. to handle both body shape and pose variations (such as SCAPE  \cite{ASK05} and SMPL \cite{bogo2016keep}) and even dynamic deformations (eg. Dyna \cite{Dyna:SIGGRAPH:2015}). Nonrigid  surface registration techniques have been used in body shape modeling to fit a template mesh models to 3D scans \cite{HAW08,ACP03, SP04, ARV07, YLSL10}.

\noindent {\bf Estimating 3D joint positions from an image } Early approaches predict 3D joint positions from key points by assuming that the almost perfect 2D key points are already extracted from an image \cite{ramakrishna2012reconstructing}. The first method based on ConvNets directly regresses 3D joint positions with an image \cite{li20143d}. Recent techniques achieves higher accuracy with an end-to-end framework that predicts 2D joints with heatmaps and then regresses 3D joint positions or depths from them \cite{zhou2017weakly, mehta2016monocular}. Martinez et al. \cite{martinez_2017_3dbaseline} on the other hand proposed a very simple network architecture that maps 2D joint coordinates to 3D joint positions, resulting in a two separate networks which can also achieve high accuracy. Pavlakos et al. \cite{PavlakosZDD16} used a volumetric heatmap representation which is a ConvNet friendly representation and can avoid regressing the real values in a highly nonlinear manner. Some methods regress kinematic parameters \cite{zhou2016deep, Skeleton_Yoshiyasu2018} to preserve human skeletal structures.

\noindent {\bf Body shape from an image } A common way to predict 3D human body shape and pose from an image is to employ pre-built statistical human models. Guan et al. \cite{Guan:ICCV:2009} first manually extract 2D keypoints and silhouettes of human body. The first automatic method was proposed in SMPLify \cite{bogo2016keep} where the human statistical model called SMPL was fitted to the 2D keypoints estimated from an image using ConvNets by an optimization technique. Tan et al. \cite{TanBC17} proposed an  indirect approach to learn body shape and pose by minimizing the estimated and real silhouettes. Tung et al. \cite{tung2017self} proposed a self-supervised learning motion capture technique that optimizes SMPL body parameters and Kanazawa et al. \cite{hmrKanazawa17} proposed an end-to-end learning system of human body and shape based on generative adversarial networks (GANs). More recently, silhouettes \cite{pavlakos2018humanshape,varol18_bodynet} and part segmentations \cite{NBF:3DV:2018} are incorporated to improve prediction accuracy. On the other hand, in DensePose \cite{Guler2018DensePose} uv coordinates and part indices are directly annotated on images to establish image-to surface dense correspondences but this is still not complete 3D representation. The most similar approach to ours would be Lassner et al. \cite{Lassner:UP:2017} where the authors proposed a method to construct a 3D human  body shape and pose dataset by fitting a SMPL model to images. Compared to them, our  approach does not require statistical 3D pose/shape priors or human interventions to validate pose fits.

\noindent {\bf Unsupervised and weakly-supervised approaches } Given 2D keypoints of human joints, Kudo et al. \cite{kudo2018} and Chen et al. \cite{Chen2019} used conditional generative adversarial networks (GANs) to estimate 3D human pose only from 2D pose dataset. Rodin et al. \cite{rhodin2018unsupervised} used auto-encoder to compress multi-view images into latent variables and they reconstructed 3D human pose from them, which does not need a large amount of 3D pose supervisions.

\section{Experimental results}
\subsection{Implementation and training detail}

Our method is implemented using Pytorch. Training takes 2-3 days using a NVIDIA Quadro P6000 graphics card with 24 GB memory. We use the Adam optimizer for all the steps in our approach. The multi-view cGANs is trained for 60 epochs with the batch size of 1024 and the learning rate of 0.0002. At each iteration, the body regressor is trained for 50 epochs with the batch size of 30 and the learning late of 0.0001. From the 1st to 4th iteration of training, we used both Human3.6M dataset and MS COCO dataset. At the last iteration (5th), we fine-tune the network on Human3.6M dataset only. We set the parameters in the loss function to $\alpha = \gamma =1$ and $\beta = 10$. For deformable surface registration, we use the learning rate of 0.1 and batch size of 10. We empirically set the parameters to $\omega_{\rm dense} = 1000$,  $\omega_{\rm KP} = 1$, $\omega_{\rm scale} = 10$, $\omega_{\rm joint}= 0.001$ and  $\omega_{\rm det} = 1$. For the first training iteration, in order to recover a global rotation, we set $\omega_{\rm scale} = 100$ and $\omega_{\rm joint}= 1$ to make the body model stiff, which is a common strategy in deformable registration \cite{ARV07}. We perform 300 forward-backward passes during the registration step at the 1st iteration. From the second iteration, 100 forward-backward passes were sufficient, since we start from the ConvNet prediction.

\subsection{Dataset}
\noindent {\bf DensePose } DensePose dataset \cite{Guler2018DensePose} contains images with dense annotations of part-specific UV coordinates (Fig. \ref{fig:dennse_annotation}), which are provided on the MS COCO images. To obtain part-specific UV coordinates, body surfaces of a SMPL human body model are partitioned into 24 regions and each of them are unwrapped so that vertices have UV coordinates. Thus, every vertex on the model have unique parameterizations. Based on this, images are manually annotated with part indices and UV coordinates to establish dense image-to surface correspondences. To use this dense correspondences in 3D model fitting, we find the closest points from image pixels to surface vertices in UV coordinates of every part. The nearest neighbor search is done in this direction because image pixels are usually coarser than surface vertices. We were able to obtain approximately 15k annotated training images with the whole body contained in the image region.

\begin{figure*}[tb]
	\centering
	\includegraphics[width=0.9\linewidth]{images/annotations.jpg}
	\caption{Dense image-surface correspondences between the template body surface and image points are found from DensePose annotations by searching nearest points in the UV  space of each body part. }
	\label{fig:dennse_annotation}
\end{figure*}

\noindent {\bf Human3.6M } Human 3.6M dataset  is a large scale dataset \cite{h36m_pami} for 3D human pose detection. This dataset contains 3.6 million images of 15 everyday activities, such as walking, sitting and making a phone call, which is performed by 7 professional actors and is taken from four different views. 3D positions of joint locations captured by MoCap systems are also available in the dataset. In addition, 2D projections of those 3D joint locations into images are available. To obtain dense annotations for this dataset, we use Mosh \cite{Loper:SIGASIA:2014} to obtain SMPL body and pose parameters from the raw 3D Mocap markers and then projected mesh vertices onto images to get dense correspondences between images and a template mesh. Note that some of the results are not well-aligned to markers and camera coordinates, resulting in a training dataset containing around $17k$ images and dense correspondence annotations.

\noindent {\bf MPII 2D human pose } The images from MPII 2D human pose dataset \cite{andriluka14cvpr} is used for testing and was not used in training. Also, 2D keypoint labels in this dataset were used to trained the cGANs.

\subsection{Protocol and metric}

We followed the same evaluation protocol used in previous approaches \cite{PavlakosZDD16,zhou2017weakly} for evaluation on Human3.6M dataset, where we use 5 subjects (S1, S5, S6, S7, S8) for training and the rest 2 subjects (S9, S11) for testing. The error metric for evaluating 3D joint positions is called mean per joint position error (MPJPE) in $mm$. Following \cite{zhou2017weakly} the output joint positions from ConvNets is scaled so that the sum of all 3D bone lengths is equal to that of a canonical average skeleton.

We also evaluate the fit of the body model to images based on the mean per pixel error and mean per vertex error which measures distances from the ground truth to the predicted vertices in 2D image space and 3D space. Prior to calculate per-vertex error, we obtain a
similarity transformation by Procrustes analysis  and align the predicted vertices to the ground truth; this is similar to the reconstruction error in the 3D joint estimation.

\subsection{Qualitative results}
In Figs. \ref{fig:Qualitative}, \ref{fig:refinment} and \ref{fig:refinment2}, we show our results on body shape and pose estimation before and after refinement. As we can see from the figures, our technique can predict 3D body shape and pose from in-the-wild images. Before refinement, the predicted poses are close to the images but still there are misalignments  especially at hands and feet. After refinement, the mesh is attracted toward image points based on dense correspondence predictions.

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.9\linewidth]{images/qualitative_results.jpg}
	\caption{Qualitative results before refinement. Our technique is able to recover body shape and pose even from in-the wild images. }
	\label{fig:Qualitative}
\end{figure*}

\begin{figure}[tb]
	\centering
	\includegraphics[width=1\linewidth]{images/comp_hmr.jpg}
	\caption{Comparisons to HMR \cite{hmrKanazawa17}. }
	\label{fig:comp_hmr}
\end{figure}

\subsection{Comparison with state-of-the-art}

\noindent {\bf 3D joint position and rotation } We compared our method with state-of-the-art techniques (Table 	\ref{tab:comparison2}). Here we only deal with unsupervised or weakly-supervised techniques which do not use full 3D supervisions. Kudo et al. \cite{kudo2018} uses conditional GANs to predict depths from 2D joint coordinates, which is the only technique that learns a model from 2D information only, except for ours. Rhodin et al. \cite{rhodin2018unsupervised} use an auto-encoder to compress visual features and reconstruct 3D pose from it, which does not require a large amount of 3D human poses. Our technique outperforms them in terms of MPJPE accuracy and is able to not only predict joints but also the orientations of limb segments as well as body shape represented in the form segment scales. Note that our 3D human pose cGANs even outperforms \cite{kudo2018} by incorporating geometric constraints. Our method is on par with HMR (unpaired) that uses 3D pose and body shape dataset for training GANs to provide 3D constraints in an unsupervised learning manner. On the other hand, we need  dense annotations on 2D images and do not use any 3D annotations such as body shape and pose. Note that HMR (paired) further provides images paired with 3D poses to do supervised learning, which makes their method  slightly better than ours but this requires an experimental setup with a Mocap system and synchronized video cameras to construct dataset.

\begin{table*}[hbt]
	\begin{center}
		\caption{Comparisons with state of the art. MPJPE [mm] is used for error metric. }
		\label{tab:comparison2}
		\begin{tabular}{c c c c c c c}
			\hline
			Kudo et al. \cite{kudo2018} & Rhodin et al. \cite{rhodin2018unsupervised} & HMR \cite{hmrKanazawa17} & HMR (paired) & Ours & Our cGANs & Ours (refine) \\
			\hline
			173.2 & 131.7 &  106.84 & 87.97 & 106.25 & 139.9 & 108.46 \\
			\hline
		\end{tabular}
	\end{center}
\end{table*}

\noindent {\bf Per-pixel and per-vertex error } In order to evaluate alignment of a body model to images, we measured the mean per-vertex error and mean per-pixel error and compare with HMR (paired),  which is shown in Table \ref{tab:pixelerr}. HMR (paired) obtained better results on Human 3.6M dataset than ours in both vertex alignment and pixel alignment, as they use a large amount of 3D pose dataset paired with images whereas ours only use 2D annotations. For this dataset, our refinement was not very effective probably because there is no large  variations of the subjects, actions and background in this dataset. For MS COCO dataset, our refinement was effective because this dataset is challenging for deep ConvNets to predict body parameters from due to a large variations in background, body shape/pose and clothing. Thanks to the refinement step, we achieve better fits in terms of the mean per-pixel error than HMR. Figure. \ref{fig:comp_hmr} shows the comparisons between ours and HMR (paired). Our method with refinement produces better alignment than HMR, especially around feet and hands. Our method captures more natural appearances of body shape and pose as the prior and constraints used do not come from the 3D dataset that is limited to some age range or captured in a controlled environment.

\begin{table*}[hbt]
\begin{center}
\caption{Comparisons to HMR (paired) in terms of per-pixel error and per-vertex error}
\label{tab:pixelerr}
\begin{tabular}{c c c c }
\hline
&HMR (paired)  & Ours & Ours (refine) \\
\hline
COCO per-pixel err. [pixel] & 13.9 & 18.6  & {\bf 12.02} \\
H3.6M per-pixel err. [pixel] & {\bf 7.3} &  9.9 &  9.2\\
H3.6M per-vertex recon. err. [mm] & {\bf 75.0}  & 102.7 & 97.2 \\
\hline
\end{tabular}
\end{center}
\end{table*}

\begin{figure*}[tb]
	\centering
	\includegraphics[width=0.9\linewidth]{images/regist.jpg}
	\caption{Ablation studies on image-surface registration. Without $\cal{L}_{\rm dense}$, the alignment to the image is poor, even the pose fit is not satisfactory. The losses $\cal{L}_{\rm geo}$ and $\cal{L}_{\rm det}$ play important role in mitigating distortions. Using cGANs to obtain depths at joints and provide soft constraints through $\cal{L}_{\rm KP}$, we can keep the depths at the joints statistically valid, which can for example prevent the incline or recline of the body model. }
	\label{fig:registration_results}
\end{figure*}

\begin{table}[hbt]
	\begin{center}
		\caption{Comparisons of MPJPE [mm] between training dataset. Note that the first iteration results are compared. }
		\label{tab:dataset}
		\begin{tabular}{c c c}
			\hline
			MS COCO & Human3.6M & Both \\
			\hline
			181.7 & 147.4 & 137.5 \\
			\hline
		\end{tabular}
	\end{center}
\end{table}

\subsection{Ablation studies}
\noindent {\bf Loss } We have compared the registration results by varying the losses (Fig. \ref{fig:registration_results}). Without $\cal{L}_{\rm dense}$, the alignment between the body model and the image is poor, even the pose fit is not satisfactory. The losses $\cal{L}_{\rm geo}$ and $\cal{L}_{\rm det}$ play important role in mitigating distortions. With our 3D human pose cGANs, the depths of a  skeleton can be constrained by making the resulting distribution close to that of the data, which can for example prevent the incline and recline of the body.

\noindent {\bf Dataset } We also compared the results of ConvNets  by changing the dataset i.e., using dense COCO only, Human3.6M only and both. The MPJPE results after the 1st iteration are shown in Table \ref{tab:dataset}. By combining MS COCO which contains in-the-wild images and Human3.6M dataset which includes domain knowledge, the better results are obtained than using a single dataset.

\begin{comment}
\begin{figure}[t]

	\subfigure[Ablation studies.]{
	\centering
	\includegraphics[width=1\linewidth]{images/ablation.jpg}

	\label{fig:ablation}
    }

		\centering
		\includegraphics[width=1\linewidth]{images/ablation.jpg}
		\caption{Ablation studies. }
		\label{fig:ablation}
	\end{figure}

\begin{figure}[htbp]

	\begin{minipage}{0.5\hsize}
		\begin{center}
			\includegraphics[width=70mm]{images/ablation.jpg}
		\end{center}
		\caption{Ablation studies. }
		\label{fig:one}
	\end{minipage}
	\begin{minipage}{0.5\hsize}
		\begin{center}
			\includegraphics[width=70mm]{images/MPJPE_graph.jpg}
		\end{center}
		\caption{History of MPJPE.}
		\label{fig:two}
	\end{minipage}
\end{figure}

\end{comment}
\begin{figure}[htbp]
	\begin{center}
		\includegraphics[width=80mm]{images/MPJPE_Graph.jpg}
	\end{center}
	\caption{History of MPJPE with respect to the number of iterations. Blue: MPJPE of ConvNet predictions on testing images; Orange: MPJPE of ConvNet predictions for training images; Gray: MPJPE evaluations of registration results on training dataset; From the first to fourth iteration, the network is trained using both MS COCO and Human 3.6M images. The fifth iteration fine-tunes the network with Human3.6M dataset only. }
	\label{fig:history}
\end{figure}

\begin{figure*}[htbp]
	\begin{center}
		\includegraphics[width = 0.85\linewidth]{images/iterative_learning.jpg}
	\end{center}
	\caption{Results after each iteration. As the number of training iterations increases, the body model fits to images better, which visualizes the effectiveness of iterative training. }
	\label{fig:vis_iterative}
\end{figure*}

\subsection{Is the iterative training strategy effective?}
To show the effectiveness of our iterative training strategy, we show a graph with the history of MPJPE errors in Fig. \ref{fig:history}. Here, MPJPE values after deformable registration is computed for training dataset. Our deform-and-learn strategy starts from image-surface registration using the T-pose as the initial pose. After the first registration phase, the train-set MPJPE for registration results is approx. 100 mm. Then, ConvNets is trained based on these registration results as supervisions. After 1 iteration, the test-set MPJPE of ConvNet predictions is 140 mm, which is slightly high. Next, deformable surface registration is performed again using the results of ConvNets as its initialization. These two steps are iterated for several times. This strategy was shown to be effective in gradually decreasing the error, which is visually noticeable in Fig. \ref{fig:vis_iterative}. In fact, MPJPE decreased approximately 30 mm from around 140 mm to 106 mm.

We also compared different training strategies in Table \ref{tab:strategy}.  For the single-step learning strategy which incorporates all the losses from Eq. (\ref{eq:loss}) for training ConvNets (including the training of a discriminator), this is a difficult problem and the error stayed high. By omitting a discriminator and the loss for joint angle regularization ${\cal L}_{\rm joint}$, we were able to train the model  but the MPJPE error was not satisfactory. Also, instead of iterating registration and learning we have tried to perform one iteration of deform-and-learn and train longer (200 epoch). This improved MPJPE slightly but not as much as five iterations of deform-and-learn. Note also that a longer deformable registration (600 iterations) in a single step only improves accuracy of MPJPE 10mm (the train-set MPJPE from 100mm to 90mm), which is inferior to our deform-and-learn strategy that can achieve the train-set MPJPE 60 mm after registration.

\begin{table*}[hbt]
	\begin{center}
		\caption{Comparisons between training strategies. MPJPE [mm] is used for error metric. }
		\label{tab:strategy}
		\begin{tabular}{c c c c}
			\hline
			Single step (Eq. (\ref{eq:loss})) & Single step (w/o $\cal{L}_{\rm adv}$ \& $\cal{L}_{\rm joint}$)  & def-learn (1 iter 200 epoch) & def-learn (5 iter) \\
			\hline
			 n/a & 148.1 & 134.8 & 106.25 \\
			\hline
		\end{tabular}
	\end{center}
\end{table*}

\subsection{Inference time}

We measure the time required for the inference phase which can be divided in to five major steps: 3D body parameter prediction, skeleton-based deformation, 2D key point prediction (optional), DensePose prediction (optional) and refinement (optional). The 3D body parameter prediction step itself only takes approx. 0.035 sec.  The time for the skeleton deformation step is also approx. 0.035 sec, which means that the inference can be performed in approximately 0.07 sec given the cropped image of a human. Other steps that are required for refinement take 0.03 sec and 0.08 sec for 2D joint prediction and DensePose prediction, respectively. The refinement step takes 5-6 seconds for 50 iterations. Adding up all the step, our technique including refinement takes around 5 seconds to process one image. Compared to SMPLify \cite{bogo2016keep} and its variants \cite{Lassner:UP:2017}, which takes over 30 sec, our technique is faster as we start from the better initial pose and shape.

\subsection{Failure cases and limitations}
As our refinement step rely on DensePose predictions, if this result is erroneousness, the final result will be worse; for example DensePose occasionally confuses the left and right for hands and feet, which results in distortions. While we represent body shape by segment scales, it is difficult to estimate a child's shape (Fig. \ref{fig:refinment}) as the body style is very different from the template mesh. A mechanism to select template meshes from different body styles would be useful for these cases. As with most of other approaches, our method cannot recover the absolute scale of body shape. Our network is currently designed for estimating body shape and pose for a single-person and we would like to extend to multiple human settings.

redpajama_set_name: RedPajamaArXiv


text:
namespace MassTransit.AzureServiceBusTransport.Topology.Builders
{
    using Entities;

    /// <summary>
    /// A unique builder context should be created for each specification, so that the items added
    /// by it can be combined together into a group - so that if a subsequent specification yanks
    /// something that conflicts, the system can yank the group or warn that it's impacted.
    /// </summary>
    public interface IReceiveEndpointConsumeTopologyBuilder :
        IServiceBusTopologyBuilder
    {
        /// <summary>
        /// A handle to the consuming queue
        /// </summary>
        QueueHandle Queue { get; }
    }
}

redpajama_set_name: RedPajamaGithub


text:
Home News Japan to restart LRT project in Sri Lanka
Japan to restart LRT project in Sri Lanka
Unilaterally terminating LRT project cost over Rs. 5 Bn to Sri Lanka: audit report
Senior Advisor to the President on Climate Change Ruwan Wijewardene says the government of Japan has expressed its willingness to reconsider starting the Light Rail Transit project, which was halted by the previous government of Gotabaya Rajapaksa.
The Japanese Ambassador Hideaki Mizukoshi has mentioned to the President's Senior Adviser that as soon as the Sri Lankan Government has reached an agreement with the International Monetary Fund, the project can be implemented again. Presidential Advisor on Climate Change Ruwan Wijewardene met with Japanese Ambassador Hideaki Mizukoshi at the Ambassador's residence earlier this week for discussions.
Mr. Wijewardene said they had discussions on few key topics and amongst them were debt restructuring, restarting projects that are on hold and how to assist Sri Lanka.
President Ranil Wickremesinghe also discussed this matter during his visit to Japan and the Japanese government also expressed its willingness to restart this project then, Mr. Wijewardene further said.
The Auditor General had revealed in a recent report that due to the abrupt suspension of the light rail project, the Sri Lankan government has incurred a loss of nearly 6 billion rupees.
The Japanese Ambassador and Mr. Wijewardene have further discussed connecting Japanese and Sri Lankan businessmen and creating awareness of investment opportunities in Sri Lanka.
President's adviser has briefed the Japanese ambassador on the proposed Climate Change University, which is planned to be built in Kandy, and on the post-COP 27 developments that took place in Sri Lanka.
Further, Mr. Wijewardena has elaborated on the Climate Prosperity Plan (CPP), and the Ambassador has requested for a mechanism to get the youth involved in the environmental work in the future.
Mr. Wijewardene also stated in the media that after the negotiations with the International Monetary Fund, the Japanese government is ready to help Sri Lanka through a number of investment projects.
Sajana Sooriyaarachchi, The Coordinating director at Presidential Secretariat, Japanese Deputy Ambassador and First Secretary Head of Political from the Japanese Embassy were also present at the discussions.
LRT project Light Rail Transit Sri Lanka
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CIS in white light CIS in blue light.
The left image shows the inside of a bladder with bladder cancer (carcinoma in situ) in white light. By Blue Light Cystoscopy with Hexvix®/Cysview®, the cancer cells light up red as can be seen on the image to the right.
Another example is the use in non-melanoma skin cancer where the cancer lesion is effectively treated leaving normal skin unharmed.


Was I so desperate to be loved? Was I so starved for the bonds of family that I would seek out captivity, and make myself a slave?
Pretty impressive, as I hadn't said where, or why, I was going. "Do you know about past lives?" she asked. I admitted I didn't. "He took you as a slave," she said. Perhaps that should have been warning enough. But I didn't believe, or understand.
Living in an abusive relationship makes life strangely simple. There's no space to think for yourself, because all your energy is focused on dancing on egg shells, trying always to be one step ahead of the lightning strike of his temper. If you can only keep him happy, then everything is all right. The world shrinks to nothing but him.
There are no choices to be made, no feelings that he doesn't dictate. Happy, sad – these are gifts he bestows. He might as well be God, because he controls everything you are. There is only one mirror in which to see yourself – and it is him.
But I had a secret place in which I could hide, my imagination. And I began to write. The words so casually spoken as I left my homeland, became a safe place I could explore. Before I met my husband, I had gone to school and studied Late Antiquity at the end of Rome. And that was where I went in my mind. I went to 420 AD.
Rome had been Christian for 30 years, and it was 15 years before the first Nicene council codified the Christian Bible. Galla Placidia, the Emperor's mother, was funding bishops to preach on Mary. She built Santa Maria Trastevere, a church dedicated to the Mother of God, right in the middle of the river. Anyone who crossed from one side of Rome to the other had to see Mary's basilica. And in seeing Mary, see Placidia, still holding on to power. Just.
On the other side of the Mediterranean the same polemic war being fought from Constantinople to Alexandria by Aelia, Flavia, Augusta and Honoria. Emperor's sisters, aunts and mothers could see the patriarchy was marginalising them in the New Faith.
They fought with all their wealth and privilege to ensure there was a woman's face amongst the pantheon of Father, Son and Holy Ghost.
The bishop Cyril preached, "Where sin was, grace abounded even more," stubbornly insisting that woman was not just Eve the temptress, but also Mary the mother.
And me, I would never be a Mother. He said he loved me so much, he couldn't stand to share me. And I knew I could never be so cruel as to inflict life with him on a child. But I imagined a girl called Aisling, Ash-- my daughter.
I knew a little about my grandmother's people. They were the Walloons, the ethnicity of the agrarian south of Belgium. And I discovered that they were the descendants of the tatters of Vercingetorix's army. Defeated by Julius Caesar, they had fled the carnage that followed the Celtic defeat. It's estimated 7 million Celts were slaughtered in the aftermath of that war. And 3 million were taken as slaves. Gaul: it meant milk skin. So Ash became a Walloon from a lost tribe of Gaul, a precious commodity by 420 AD.
And slowly my novel became fleshed out. In my own world, slavery was a very personal story. The worst slavery of all, liberty of mind. When he hit the walls, and said he had to let it out, he had a stressful job… I really only remember the first time. And even that memory is a blur. I hid.
When the shouting stopped I came out and said that was terrifying. But somehow he always had the words, events got twisted, and somehow the way he saw things was the way they were. And after five years, he'd convinced me I was crazy. He never hurt me. He said so. So it must be true. Words, twisting things, they shape the world. Lies are the truth; they hold us down.
And Ash, I realised, must be a word thief. Who could defy Rome? What kind of person could defy slavery even if she found herself in chains? I was 14,000 miles from my family, and my friends. In my loneliness I made Ash the outcast of her tribe, she stole their songs and stories. And that was how she could steal Latin from her captors and learn to speak. She could remember. The Celtic bards could recite poems that took three days to tell. Ash could remember when I couldn't.
After five years of living with him, it was Urzhuli Freda, the Vodun Goddess of ardour, Venus, who sent me a reality pill, sugar-coated in the shape of a hot, 21-year-old mechanic. It was lust at first sight. And it was my passion, my new mirror-man that showed me a new self.
And that woman, that vamp, she fought back. I started screaming and I punched a wall too. But unlike my husband's, my fist went straight through the plaster board. And that was my revelation. He never left any signs for anyone to see what was happening in his house. But, in one punch, I left a gaping hole. I knew in that instant that he never lost control. It was a lie. His temper was always about frightening me into submission.
He said, "I'm not pushing, I'm not pushing," in a sing-song voice, waving his hands theatrically. Another revelation.
It wasn't just my Mother's voice he was using, her belittling sarcasm, her way of tearing down my subjective reality; it was even her manner. I'd seen that sinister, comical dance before. And suddenly I knew why I was there with him. I'd married my Mother.
But the vamp I'd become, she held on. She knew she'd been pushed, and his words couldn't undo that. "'I'm not pushing' is not an appropriate response to 'Don't push me,'" I said, pouring acid into every word.
He got out one of his guns, the magnum 45. They're bigger in real life than they are on TV. Cameras don't make guns put on ten pounds. He didn't seem to understand that the life we had together meant nothing to me after I'd known the heat of Urzhuli, after wrapping my body around another man.
I walked right into that gun. I climbed into his lap and said, "Make it a clean shot." I called his bluff. He was a coward, and a bully, and I knew it. If he pulled the trigger I didn't care. I just wanted to know he'd be covered in my blood and brains. I wasn't afraid anymore.
That girl never came back. She died in that house. She haunts me.
Twenty years have gone by, and I realise my longest relationship is with Ash. She has gone through the darkness with me, and she taught me how to hold on when all hope is gone. She showed me the beauty of the world, and the wisdom in an acorn and a salmon swimming upstream. Her gods are mountains, the wind, the rain; she is a Pagan and it is the beautiful green world that is her religion.
I fleshed out my research. And going home to Australia, I watched my Mother die of dementia and leukaemia. And I understood, I forgave.
We assume we can speak love. But I certainly don't think I was born knowing Swahili, for instance. We speak the love we learn. And I know she did the best she could, from her childhood of horrors. And as she died, I accepted that we are animals dreaming we are gods.
My Mother was never my Mother. But Ash taught me about the Goddess, and all her faces. She is within us all. There is in every woman, something sacred, magic. We are the creators, the gardeners who grow a man's seed to fruition.
I have been reborn. As they held my son up for me, he opened his eyes and they crackled with electric blue fire. Drunk on the pain of birth, I looked up at him, and he seemed ten feet tall, enormous, a deity. Then this creature, who had been yanked from my body with forceps, let out a gasp, a cry of bewilderment.
In that instant, we were the only two people in the world. All questions, with no answers. I was transformed: I became the Mother. I left Eve behind, and chose Mary.
This place I came from was nowhere like the places you know. There were no cobbled streets, or marbled piazzas. No town square, no town. Just the Long House surrounded by the village, hidden amongst the trees. Trees that stretched on unbroken to the mountain's peak.
Our paths were made by the tramp of constant feet, and the deer we hunted. Not by clever men and slaves who tamed rock into roads.
Then the bear men came, mercenaries hired by Rome. They slaughtered the Lassuni. They kept only us alive, the children. They call us Gauls, milk skins. They plan to sell us into slavery to pay their wages.
But I am Tchona. I am the outcast of my tribe, the daughter of the Standing Stone Cairn Egroagh Bragh. I am a word thief. I have spent my whole life stealing stories and songs. I will steal the language of my captors.
I do not concede that my people's war is lost, until I stop fighting. I have found myself in chains, but I will never be a slave.
Courtenay Trinder completed her B.A. in Art History and Curatorship in 1994. Since then, she has worked as a phone psychic, a dog walker, an art teacher for pre-schoolers, and at the Canberra Theatre. The Weight of Lies, her first novel, took twenty years to finish, and is available here. She lives with her whippet Tinker, a Rabbit that looks like Hitler, and a beautiful little boy who has taught her everything she knows about love.


Global Infrastructure completed a £2.5m water main project on Skye for Scottish Water. The project connected the remote communities of Elgol, Stollamus and Torrin to a central Water Treatment works in Broadford, with each location having their existing treatment facility (Inverness Filter) converted to water storage.
This environmentally sensitive project saw us lay 27.6km of water main through various island landscapes, which were reinstated to their original condition and to the satisfaction of all stakeholders concerned.


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Naomi Shiner is a Senior Lecturer at the University of Derby, teaching both undergraduate and post graduate courses.
She has had had a varied career since qualifying as a Radiographer in 2000. Naomi spent eight years working in a busy teaching hospital, the Queens Medical Centre, Nottingham, progressing to become a Senior Radiographer.
She decided to extend her radiography skills from acquiring images to interpreting them. Gaining a post graduate qualification, Naomi moved into advanced practice and became a Reporting Radiographer in 2003.
She says she loved the challenge of working with radiologists and making a real difference to the service. It was at this point through helping junior doctors and other healthcare professionals to improve their radiographic interpretation skills that she realised that she wanted to teach.
Naomi can now pass on her knowledge to other radiographers studying postgraduate programmes.
Radiography allows you to move around the country and world with ease, so in 2008 Naomi moved to Scotland to work in computerised tomography (CT) and Magnetic Resonance Imaging (MRI) and she became the first autonomous reporting radiographer in the Highlands. Whilst developing and supporting this service, Naomi moved into the superintendent role of the conventional imaging department in 2010.
In 2013 it was time to take the plunge into academia. Naomi moved to Robert Gordon University in Aberdeen and she says it was 'like a duck to water'.
Not all Naomi's time is spent directly with students. There are networking opportunities, visiting placement sites, conferences and working across professions. This enables her to develop further and focus research to what interests her most.


Beauty Blog by Angela Woodward: How do I get my hair to look like it does after going to the salon?
How do I get my hair to look like it does after going to the salon?
This is a SUPER common question, and I always have the same answer: Product and Practice.
My pet peeve is when my clients say "I went home and tried that technique you showed me, but it didn't work!" when I know you are not using the right products. I am not a magician, and granted I am knowledgeable in this industry, but I don't have super powers. I went to school for 2000 hours (2 years) and spent a lot of time learning about the structure of hair and the chemical reactions and processes that hair goes through. I also study ingredients. I study what ingredients do what, and what fillers and things you should look for and (watch out for) in your skin and hair products. It makes a difference. I use high end products myself, and that is because I understand, know and trust them. My point is, don't be surprised that your hair doesn't look as good when I am using a $25 heat protecting lotion and a $19 mousse, while we both know you are using Garnier Fructis and Suave at home.
Practice makes perfect! I did not start school knowing how to do a great round-brush blow dry. I had to work really hard and practice A LOT (for two years!) to get it right. So get onto youtube and look up "How to Round Brush your own Hair". You can find tutorials for all different textures and lengths. Then practice, fail, practice, give up (temporarily), check facebook and practice some more. Rome wasn't built in a day, and neither is the ability to do a killer beach wave!
Not everyone's hair is the same, it's so important to treat your hair accordingly and not to copy someone with a different hair type!


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Latest News / Russia / Russia-Ukraine War
South Korea may soon overtake Russia as one of the largest arm exporter in the world
October 27, 2022 - by Ankit Verma - Leave a Comment
Since the beginning of the Russia-Ukraine War in February this year, it has been very difficult for the russian arms industries to keep the production lines on. Russia has suffered heavy losses in the ongoing war, the Ukrainian Army supported by modern anti-tank weapons supplied by NATO and the USA has destroyed huge columns of russian armoured vehicles, currently russia is struggling to fill the gap in its own armed forces. Simply speaking, the Russian Armed Forces are facing a series of difficulties to meet its own huge demands. Plus, the western countries and USA has imposed sanctions on Russia, such as stopping the supply of semiconductor chips to Russia. Forcing Russia to stop producing high-tech weapons, meanwhile on the other side, South Korea is emerging as a big arm exporter. Maybe in the future, South Korea will replace Russia as one of the largest arm exporter in the world.
The Russian-Ukrainian conflict is escalating, and a large amount of Korean weapons are rapidly pouring into NATO countries. NATO countries such as Poland, Turkey, Finland, Norway and Estonia are importing different types of Korean weapons in large quantities.
By 2022, South Korea's arms exports will be fourth in the world. Poland has directly ordered from Korea 180 K-2 Panther tanks, 212 K-9 155mm self-propelled guns, 48 FA-50 fighters and 288 K-239 missile launchers.
At the same time, Poland also signed a contract to co-produce 640 K-2 main tanks and 428 K-9 155mm self-propelled howitzers. Undoubtedly, European countries increased their purchases of Korean weapons, in order to increase their defense potential against any incoming threat.
And Russia has long been a manufacturing powerhouse; list of Russian weapons, such as Dagger hypersonic missile, Iskander ballistic missile, Su-57 stealth fighter, Su-35 fighter, S-400, S-350 air defense missile, T-tank 90M, 2S35 Koalitsiya-SV self-propelled howitzer… has advanced features and also are very affordable.
Therefore, Russia always maintains the second position in the world in terms of arms production and export, just behind the US. However, the Russian military industry faced the problem of insufficient production capacity. In addition, there is a harsh embargo from the West, causing many components and spare parts to be seriously lacking.
In 2022, the Russian Ministry of Defense ordered 400 T-90 Main Battle Tanks, which will have to be delivered by 2024. In addition, the Russian Army will upgrade 800 T-62 tanks, but this order will not be completed until 2025.
In particular, Russia's defense industry currently lacks the ability to produce various types of precision-guided bombs and cruise missiles in large quantities; In addition, the production capacity of armored vehicles has also been seriously reduced.
The decline in Russia's defense production capacity is primarily due to the operating mechanism. For example, in Soviet times, the tank repair plant of the USSR Ministry of Defense had a huge military maintenance system. This agency is responsible for low cost maintenance for units throughout the army.
However, since Russia's military reform, everything has been commercialized and carried out according to the model of the US Army; i.e. the supplier of the weapon will be the one that warrants and repairs the weapon. This led to the dissolution of the technical guarantee agency in the Russian army.
Besides, Russia's defense production capacity is low; It should be remembered that the Soviet Union was the first country to invent a complex of active protection for tanks, but many main battle tanks on the Russian-Ukrainian battlefield were not equipped with an active defense system.
Many new Russian weapons are very advanced, but the production number is not enough; therefore, the Russian army has to use a large number of old tanks such as T-62 and T-72, BMP-2 infantry fighting vehicles and Su-25 attack aircraft on the battlefield; as well as a large number of artillery shells and unguided bombs.
Meanwhile, Russia's opponent, the United States, also has a serious shortage of weapons when it has to "slope" aid to Ukraine. The US military has ordered 700 more HIMARS high mobility multiple launch rocket systems (MLRSs) and Poland will have to wait at least until 2029-2036 to get them.
To meet defense requirements, Poland purchased the Korean version of the MLRS, which is the K239 Chunmoo wheeled modular missile launcher system with American technology; in fact it is a copy of the US HIMARS system, when the two systems can share each other's ammunition.
Missiles of the K239 Chunmoo system have a range of up to 80 km and tactical missiles have a range of 160 km and have a satellite-guided version of the HIMARS M31. Korean weapons are essentially "American weapons", but with faster delivery and cheaper prices.
TaggedRussian armySouth koreausa
In terms of developing Mach 6 missiles, India is now ranked fourth in the world.
Indra will update the ground radars of the Spanish air defense.
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"Environmental richness" is also called biodiversity, or biological diversity. Environmental richness is used to describe the variety and interconnectedness of life on Earth. It includes genetic diversity, species diversity, and ecosystem diversity around the world. Preserving environmental richness is important as human populations and activities increase. Threats to biodiversity include habitat loss and destruction, alterations in ecosystem composition, the introduction of non-native species, over-exploitation, pollution, and global climate change. Humans depend on biodiversity for our cultural, economic, and environmental well-being. For example, healthy, biodiverse ecosystems can prevent flooding, erosion, and other natural disasters. These ecosystems also affect our climate, including air and water quality.
The United States Environmental Protection Agency (EPA) was created by the White House and Congress in July 1970 to repair environmental damage and establish new criteria to reduce future environmental problems.
The United Nations declared 2010 to be the International Year of Biodiversity.
National Geographic supports The Green Wave, a global campaign that promotes the importance of biodiversity around the world and focuses on the crucial role that biodiversity plays in our lives and our futures.
number of ecosystems present in a specific area.
difference or variety of units of inheritance (genes) in a species.
formula that calculates the total number of specific species in an area compared with the number of all species.


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«Show Biz Kids» — песня, написанная Уолтером Беккером и Дональдом Фейгеном и исполненная группой Steely Dan совместно с Риком Дерринджером на слайд-гитаре. Она была выпущена ведущим синглом с альбома 1973 года Countdown to Ecstasy и добралась до 61-ой позиции в хит-параде Billboard Hot 100.

Участники записи

 Дональд Фейген – пианино, основной вокал
 Уолтер Беккер – электробас, гармоника, бэк-вокал
 Джим Ходдер – ударные, перкуссия, бэк-вокал
 Рик Дерринджер – слайд-гитара
 Виктор Фельдман – маримба, перкуссия
 Шерли Мэтьюз, Мирна Мэтьюз, Патрика Холл, Ройс Джонс, Джеймс Роллестон – бэк-вокал

Кавер-версии
Песня была сэмплирована группой Super Furry Animals на их сингле 1996 года "The Man Don't Give a Fuck".

Песня также была перепета Риккой Ли Джонс для своего альбома 2000 года It's Like This.

Примечания

Доп. ссылки

 Song Review by AllMusic

Песни 1973 года
Песни Steely Dan
Синглы ABC Records
Синглы 1973 года

redpajama_set_name: RedPajamaWikipedia


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text:
HomeOp-ed
'Evil, Russian style': Fake Putin ice-hockey conspiracy theory combusts
Bryan MacDonald is an Irish journalist based in Russia. He has written for RT since 2014. Before moving to Russia, Bryan worked for The Irish Independent, the Evening Herald, Ireland on Sunday, and The Irish Daily Mail. Follow him on Twitter @27khv
Russian President Vladimir Putin participates in a training of the Night Hockey League at the Galaktika center in Krasnaya Polyana. © Aleksey Nikolskyi © Sputnik
For the benefit of the uninitiated, the KHL is the world's second-most prestigious ice-hockey competition after the famous NHL. It's also a bit more international because, while its North American cousin is restricted to Canadian and US teams, the KHL features clubs from Belarus, China, Finland, Kazakhstan, Latvia and Slovakia.
However, the dominant nation is Russia, and teams compete for the Gagarin Cup – a trophy which is obviously named after the legendary Soviet cosmonaut.
Last month, a US-based sports journalist flew a KHL-related kite on Twitter, which received a stratospheric reception, garnering thousands of retweets. However, unlike Yuri the spaceman, who enjoyed a safe landing, this particular projectile crash-landed. And its author, normally a prolific tweeter, has failed to correct the record.
The chap concerned, one Slava Malamud, alleged that the outcome of this season's KHL had been pre-determined. And that SKA, of St. Petersburg, was practically guaranteed to win the championship because they were "(Vladimir) Putin's team, (and) this is Putin's election year."
1/ So, here is what's happening in the KHL, for those who still can't quite grasp the banality of evil, Russian style... The entire league has been set up this year to allow SKA to win. It's Putin's team, this is Putin's election year, it's chock full of OAR Olympians...
— Slava Malamud (@SlavaMalamud) March 8, 2018
This was only the tip of the iceberg, however, because the rant was dedicated to those "who still can't quite grasp the banality of evil, Russian style" and couldn't fathom how "SKA must win. It's not an option." And the reason? Because "most of its players are rabid Putin supporters who took part in his campaign rally last week. I repeat: SKA must win. It's not an option…"
Malamud did concede that (henceforth to be known as 'tweet three') "there is a slight possibility of a loss, if the opponent is CSKA, owing to the Moscow team's legacy and the "patriotic value" of their potential victory, but it's a small chance. Putin wants SKA, and SKA it shall be…"
The thread goes on to allege that SKA's last-16 game with Severstal Cherepovets was fixed and that officials bent the rules to ensure a victory for the St. Petersburg outfit, likening the smaller club to the Charlestown Chiefs from the 1997 movie 'Slap Shot.'
Malamud's 21-tweet tale garnered over 5,000 retweets for its first salvo, right down to around 580 for its final blast. And, as well as being shared by Russia focused journalists who should know better, it was picked up by the wider media. For instance, Paste Magazine told its readers "How Vlad (sic) Rigged the Russian Hockey Season for His Favorite Team" and Irish sports site balls.ie went with "Russian Journalist Claims That Putin Has Rigged Russian (sic) Hockey League."
Perhaps more incredibly, Canadian state broadcaster CBC granted the author a 24-minute interview to deliver his conspiracy theory to a huge audience. And Kevin Rothrock, an editor at Meduza (a Latvia-based Russian news site which won't reveal its funding), conducted a particularly grating interview with the hockey delusionist, the gruesome twosome luxuriating in a fog of fantastical Kremlin nefariousness.
'Football team would be better option' – Russia UK Embassy trolls British tabloid over WC 'spies'
Right from the start, I was suspicious of the thread. For a variety of reasons. Among them, the ferociously competitive nature of the KHL, allied to the fact that there's no evidence Putin is some sort of SKA super-fan, nor, indeed, much to suggest he's a supporter of the St. Petersburg outfit at all. Furthermore, it also struck me as ludicrous how the president, during an election year, with the war in Syria and economic challenges at home, would be bothered to manipulate the outcome of a professional sports league.
Thus, I kept an eye on the play-offs and wasn't hugely surprised when CSKA eliminated SKA in the semi-finals by four games to two. At this point, I called out the thread on Twitter, only to be met with an emotional response from the author.
Me: "Last month this thread got 5,000+ retweets. It suggests that Russia's KHL hockey league was set up this year to allow SKA (St. Petersburg) to win. Because it is 'Putin's team.' SKA lost the playoff semi-finals to CSKA (Moscow) yesterday."
Malamud: "Reading all the way to Tweet number 3 was hard, I suppose." (Tweet three, as noted above, stated that CSKA had "a small chance").
It's not about you. It's about how people love to believe the worst about Russia. Hence the number of retweets. i found your thread amusing. Still do.
— Bryan MacDonald (@27khv) April 9, 2018
Me: "It's not about you. It's about how people love to believe the worst about Russia. Hence the number of retweets. I found your thread amusing. Still do."
Malamud: "There isn't anything there that isn't true. And Russia's current regime is quantifiably evil, inasmuch as it promotes and perpetuates lack of freedom, supremacy of state, intolerance to minorities, and aggressive, conquest-focused militarism."
It's fair to say the exchange escalated quickly. But it also betrayed the conspiracy theorist's motives. Because, clearly, this tall tale wasn't really about the KHL, it was rather the ideologue venting his animosity, enmity and aversion to Russia, with hockey merely providing a useful backdrop.
But this didn't stop thousands of Twitter users spreading the disinformation, and mainstream media outlets jumping on the bandwagon. This backs up my belief that a great many people will latch onto anything showing Russia in a bad light. And, luckily for them, there's a substantial pool of activists, masquerading as journalists and academics, ready to feed their prejudices. And these goons have poisoned the information space to such a degree that casual observers can't separate distortions from accurate reportage.
Anyway, CSKA didn't win the final. Instead, they were crushed (4-1) by AK Bars of Kazan, who earned their third title in 10 years, last weekend. Which means the Tatarstan outfit clearly also forgot about the importance of "tweet three."
The statements, views and opinions expressed in this column are solely those of the author and do not necessarily represent those of RT.
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2011 was an excellent year for wool with strong demand for wool products and raw wool in the first half of the year, soaring prices for competitive fibers and low wool supply combining to bring the highest wool prices in Australian dollars since 1988 and record prices in U.S. dollars.
Demand weakened in the second half of the year as a result of a slowdown in the European and U.S. economies. Supply, however, remained tight.
Prospects for the wool industry in 2012 will be dominated by concerns about the European and U.S. economies. However, low competitive fiber prices and very tight wool supplies will also be major and conflicting factors in the direction of wool prices.


A few years ago when ESPN was handing out pink slips like struggle rappers do their mixtapes people were eagerly waiting word that the much revered but also reviled Stephen A. Smith was one of the many ESPN personalities making their way to the unemployment line. It didn't happen. Now a few years later ESPN is about to double down on their most famous/polarizing on-air sports analyst.
According to the New York Post ESPN is negotiating a new deal with the First Take co-host and word is that salary will be somewhere in the ballpark of $10 million per year.
No disrespect but are the ratings still that good? Just askin.' Still, much props on the well deserved pay raise to the Queens native and we hope he's prepared for the kind of hate that will follow when word spreads that he's ESPN's highest paid on-air personality.


This Halloween, come and join us in Letchworth Town Centre for family-friendly alternatives to the traditional door-to-door trick-or-treat night.
Visit the Haunted House, Leys Square.
Monster Mitts Workshop, Garden Square Shopping Centre.
Street Halloween Close-up Magic, Leys Square.
Halloween Night – Town Walk, Leys Square (meet at 3:30pm).
Children can dress in costume and safely collect sweets and other goodies from businesses in the town, accompanied by an adult.
*Children can bring their own pumpkin for the walk (please bring battery operated tealights).


text:
Talented Black Filmmaker, Derrick Muhammad, Launches Book Tour
Nationwide — Georgia filmmaker and author Derrick Muhammad has released his long-awaited autobiographical book, Guerilla Film Grind, detailing his experiences in the often very turbulent world of filmmaking.
The book traces the origins of Muhammad's film career from his time as a young man coming of age in Columbus, OH to his challenging days in Hollywood as an aspiring writer and director. It serves as a guide for aspiring filmmakers who are unsure where to start or what direction to move in.
His approach has been described as "raw, uncut, honest and in your face".
"Guerilla Film Grind was born out of my desire to share my philosophy on how to make a movie outside the Hollywood parameters… My goal is to strip away all excuses and give you a step by step process from pen to distribution," Muhammad says.
Currently, he is scheduled to speak in cities across the United States to promote his book and he is also looking for venues and media outlets to host or feature him.
"The filmmaking industry is competitive and it cost money to compete. This book will not only guarantee you a place in the game but it will let others know that the real competition is you," he adds.
The book is available for purchase on Amazon or at DerrickMMuhammad.com
Journalists and bloggers who would like to interview Mr. Muhammad or organizations/ businesses that would like to host him should contact his office at (818) 922-9488.
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