text:
\section{Introduction}\label{sec:introduction}}

\IEEEPARstart{G}{enerating} photo-realistic images or videos of human faces has lately become a very popular computer vision topic, with a remarkable amount of research and discussion revolving around it. Aside from considerable advancements accomplished by the computer graphics community \cite{thies2015realtime, face2face, thies2018headon}, deep neural networks and more specifically \textit{Generative Adversarial Neural Networks (GANs)} \cite{goodfellow2014generative} have made a significant contribution towards this direction. For instance, \textit{StyleGAN2} \cite{stylegan2} has shown incredible results on unconditional faces synthesis

Neural talking head synthesis, here also referred to as \textit{reenactment}, is the task where a reference (source) image or video is manipulated or re-animated to match with the facial expressions and head poses performed by a driving (target) actor. Reenactment systems have numerous applications in social media, teleconference, image and video editing, as well as virtual reality and games. They can be further used for facial video compression and reconstruction \cite{wang2021facevid2vid}.

\begin{figure}[h!]
\centering
\includegraphics[width=0.44\textwidth]{figures/teaserv2.png}
\caption{Given a source and a target image, first we compute a flow field to warp the source image, according to the target pose, expression and gaze. Then, a rendering network generates the final image based on the warped image (and warped visual features). We condition synthesis on sketches of 3D key-points. We preserve identities, by adapting the target key-points to the facial shape of the source, using a canonical 3D key-point estimator. We control gaze in the generated faces by color coding the interior of the eye cavities in sketches with the gaze direction angles.
}
\label{fig:canonical}
\end{figure}

Learning-based reenactment methods are classified either as \textit{person-specific} or \textit{person-generic}. On the one hand, person-specific approaches \cite{kim2018deep,head2head2020,head2headpp} are based on identity-specific neural networks. Despite the impressive visual results, these networks require re-training using a long video of the particular source actor we wish to reenact. On the other hand, person-generic systems \cite{Zakharov2019FewShotAL, fsvid2vid, bilayer, MarioNETte:AAAI2020, X2Face, Siarohin_2019_CVPR, FOMM, wang2021facevid2vid, doukas2020headgan} have the ability to adapt to the source identity during inference, given only a few reference images of the subject, even a single one. Although very promising, in general such models generate samples of lesser quality, when compared with their identity-specific counterparts.

Another important aspect of talking head synthesis relates to face modeling. A common face representation is 2D sparse landmarks (68 points), as used in \cite{Zakharov2019FewShotAL, fsvid2vid, bilayer}. More recent methods, such as \cite{MarioNETte:AAAI2020} and \cite{Geng2018WarpguidedGF} use 3D landmarks instead. Among other benefits, the selection of a three-dimensional representation enables \textit{free-view control}, as we can manually intervene in the head pose to be generated. Other widely used facial representations are dense 3D facial meshes combined with statistical priors of the face, such as \textit{3D Morphable Models (3DMMs)} \cite{3dmms, 3dmmsbooth, Booth, Booth_2016_CVPR}, which are adopted for reenacting heads in \cite{kim2018deep, head2head2020, doukas2020headgan}. This modeling enables to disentangle shape (identity) from expression-related deformations, and therefore helps to tackle the \textit{identity preservation problem} in cross-identity motion transfer, which is neither straightforward nor trivial with sparse landmarks. In a quite different line of work, Siarohin \textit{el al.} \cite{Siarohin_2019_CVPR, FOMM} attempt to solve the most general problem of motion transfer for arbitrary objects, proposing a \textit{model-free} method based on unsupervised 2D key-point detection. Following up, Wang \textit{el al.} \cite{wang2021facevid2vid} present a model that focuses on faces and learns to detect \textit{canonical} 3D key-points without supervision, as a way to disengage identity from expression and pose.

Given that they deal with motion transfer, most recent systems are flow-based \cite{MarioNETte:AAAI2020, Geng2018WarpguidedGF, X2Face, Siarohin_2019_CVPR, FOMM, wang2021facevid2vid, doukas2020headgan}, meaning that they make use of optical flow to warp the source image(s) or visual features into the target facial pose and expression, as a first step prior to image synthesis. The aforementioned model-free approaches \cite{Siarohin_2019_CVPR, FOMM, wang2021facevid2vid} are able to learn important key-points for motion, as they approximate optical flow in areas near these points. For example, \cite{FOMM} and \cite{wang2021facevid2vid} use a \textit{first order approximation of motion}. On the contrary, methods such as \cite{X2Face, MarioNETte:AAAI2020, doukas2020headgan} learn a \textit{dense optical flow} in all spatial locations, without resorting to approximations.

In this work, we do not use a first order approximation of motion, but rather use a neural network that learns to predict dense optical flow, similarly to \textit{HeadGAN} \cite{doukas2020headgan}. We propose \textit{Free-HeadGAN}, a system based on the architecture of \cite{doukas2020headgan} which is "free" from statistical priors of faces like 3DMMs. In order to overcome the identity preservation obstacles in reenactment and inspired from \cite{wang2021facevid2vid}, we propose a network for regressing 3D key-points along with head pose and expression deformations. Different from \cite{wang2021facevid2vid} our method does not predict canonical key-points directly. Instead, we compute them based on the estimated 3D points after removing pose and expression. Moreover, we supervise our key-point detector with pseudo ground-truth 3D facial landmarks extracted with \textit{RetinaFace} \cite{RetinaFace}. In this way, we ensure that key-points are always placed on meaningful parts of the face, avoiding cases where no key-points are assigned to important regions such as the eyes and mouth, as it commonly happens with unsupervised models \cite{Siarohin_2019_CVPR, FOMM, wang2021facevid2vid}. Lastly, we propose a gaze estimation network that makes explicit eye gaze transfer possible. The contributions of our work can be summarised in the following:
\begin{itemize}[leftmargin=*]
    \item We release \textit{HeadGAN} \cite{doukas2020headgan} from its 3DMM priors, using sparse 3D landmarks. We achieve comparable and in many cases improved results, as suggested by our qualitative and quantitative experiments.
    \item We design a module that performs disentanglement of identity, expression and pose through the computation of canonical 3D key-points.
    \item We propose a network that regresses 3D meshes of the eyes. We use these meshes to obtain the direction of gaze, which is then used to condition image synthesis. To the best of our knowledge, we deliver the first person-generic reenactment system with explicit gaze control.
    \item We show a N-shot extension of our framework.
\end{itemize}

\section{Background}\label{sec:Background}

\begin{table*}[!t]
\renewcommand{\arraystretch}{1.15}
\caption{Checklist of key features and design choices of state-of-the-art head synthesis systems that support full head animation and  reenactment}
\label{table:background}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Method} & Person & \multirow{2}{*}{N-shot} & Face & Free-view & Identity & Gaze & Warping & \multirow{2}{*}{Sequential} \\
 & generic & & modeling & control & preservation & control & flow & \\
\hline
\textit{DVP} \cite{kim2018deep} & \xmark & - & 3DMM & \cmark & \cmark & \cmark & \xmark & \xmark \\
\textit{Head2Head} \cite{head2head2020} & \xmark & - & 3DMM & \cmark & \cmark & \cmark & \xmark & \cmark \\
Zakharov \textit{et al.} \cite{Zakharov2019FewShotAL} & \cmark & \cmark & 2D landmarks & \xmark & \xmark & \xmark & \xmark & \xmark \\
\textit{Few-shot vid2vid} \cite{fsvid2vid} & \cmark & \cmark & 2D landmarks & \xmark & \xmark & \xmark & \xmark & \cmark \\
Zakharov \textit{et al.} \cite{bilayer} & \cmark & \xmark & 2D landmarks & \xmark & \xmark & \xmark & \xmark & \xmark \\
\textit{MarioNETte} \cite{MarioNETte:AAAI2020} & \cmark & \cmark & 3D landmarks & \xmark & \cmark & \xmark & learned & \xmark \\
\textit{Warp-Guided GAN}s \cite{Geng2018WarpguidedGF} & \cmark & \xmark & 3D landmarks & \cmark & \xmark & \xmark & crafted & \xmark \\
\textit{X2Face} \cite{X2Face} & \cmark & \cmark & embeddings & \xmark & \xmark & implicit & learned & \xmark \\
\textit{Monkey-Net} \cite{Siarohin_2019_CVPR} & \cmark & \xmark & 2D key-points & \xmark & \xmark & implicit & approx. & \xmark \\
\textit{FOMM} \cite{FOMM} & \cmark & \xmark & 2D key-points & \xmark & \xmark & implicit & approx. & \xmark \\
\textit{face-vid2vid} \cite{wang2021facevid2vid} & \cmark & \xmark & 3D key-points & \cmark & \cmark & implicit & approx. & \xmark \\
\textit{HeadGAN} \cite{doukas2020headgan} & \cmark & \xmark & 3DMM & \cmark & \cmark & \xmark & learned & \xmark \\
\textit{Free-HeadGAN} & \cmark & \cmark & 3D landmarks & \cmark & \cmark & \cmark & learned & \xmark \\
\hline
\end{tabular}
\end{table*}

\subsection{GANs and Image Synthesis}

Since their introduction, \textit{GANs} \cite{goodfellow2014generative} have been extensively used both for unconditional and conditional \cite{mirza2014conditional} data synthesis. They have been successfully applied to various computer vision tasks, such as image-to-image \cite{pix2pix2017, CycleGAN2017, wang2018pix2pixHD, park2019SPADE}, video-to-video \cite{wang2018vid2vid, fsvid2vid} and audio-to-image translation \cite{Vougioukas, thies2020nvp}. In this work, we focus on the problem of conditional image synthesis and propose a GAN-based talking head synthesis system that achieves high photo-realism and improved identity preservation compared to current state-of-the-art approaches.

\subsection{Talking Head Synthesis}

\subsubsection{Face Reenactment}

Most of the early attempts in human face synthesis focus on the task of \textit{face reenactment}, which aims to transfer the facial expressions performed in a driving video to the face of another person. The majority of face reenactment methods, i.e. \textit{Face2Face} \cite{face2face}, manipulate the source footage, by re-writing only the facial region of the source video stream, while keeping the remaining parts (e.g. hair, body, background) unchanged \cite{GVRTPT14, obama}. A first exception to those systems has been \textit{Bringing Portraits to Life} \cite{elor2017bringingPortraits}, which is able to slightly animate the entire head with the application of 2D warping on the source image.

\subsubsection{Full Head Reenactment}

\textit{Deep Video Portraits (DVP)}~\cite{kim2018deep} is allegedly one of the earliest learning-based reenactment methods, as it utilises an image translation network that manages to fully transfer the head motions of the driver to the source, including eye gaze. In a more recent work, \textit{Head2Head}~\cite{head2head2020, head2headpp} adopts a sequential video-based translation network that performs \textit{full head reenactment} while taking into consideration the temporal dynamics of talking faces. Both aforementioned methods rely on 3D information of faces and strong statistical priors, such as \textit{3DMMs} \cite{3dmms, 3dmmsbooth, Booth, Booth_2016_CVPR}. Despite their remarkable results, these \textit{person-specific} models are optimised on long videos of the source identity, being incapable of adapting and generalising to new identities without re-training.

On the other hand, \textit{person-generic} methods are able to adapt their generative process on the appearance of new source identities, even if they are not introduced to them during training. Numerous identity-agnostic talking head synthesis systems are 2D-based \cite{Zakharov2019FewShotAL, bilayer, fsvid2vid, MarioNETte:AAAI2020}, as they condition image synthesis on 2D facial landmarks. Zakharov \textit{et al.} \cite{Zakharov2019FewShotAL} introduce a few-shot framework that is composed of an identity embedding network and a generator with AdaIN \cite{adain} layers for receiving the embedding vectors. They propose a training strategy consisting of a meta-learning stage which involves optimisation on a multi-person dataset, followed by fine-tuning on an few images of a new and unseen face. The video-based system \cite{fsvid2vid}, namely \textit{few-shot vid2vid}, employs a sequential generator equipped with dynamic SPADE \cite{park2019SPADE} layers, enabling to adapt synthesis on the appearance of new reference images. Zakharov \textit{et al.} \cite{bilayer} propose a SPADE-based system that achieves real-time one-shot talking head synthesis on mobile phones, focusing on the foreground as it disregards background information. In contrast to 2D face modeling, 3D-based approaches such as \textit{Warp-Guided GANs} \cite{Geng2018WarpguidedGF} and \textit{MarioNETte} \cite{MarioNETte:AAAI2020} depend on 3D landmarks to represent faces. As opposed to the preceding works, \textit{MarioNETte} \cite{MarioNETte:AAAI2020} applies a PCA-based disentanglement on identity and expression with 3D facial landmarks, in order to alleviate the identity mismatch problem during reenactment. \textit{HeadGAN} \cite{doukas2020headgan} is among the first one-shot talking head synthesis systems that relies on identity and expression 3DMMs for the disentanglement of expression and identity parameters in dense 3D shapes. \textit{HeadGAN} yields image samples of unprecedented quality in reenactment, while maintaining the identity of the source and exhibiting free-view control.

There is a considerable amount of research involving person-generic \textit{model-free} head synthesis \cite{X2Face, Siarohin_2019_CVPR, FOMM, wang2021facevid2vid}. \textit{X2Face} \cite{X2Face} is one of the pioneering approaches on the problem, which does not rely on any 2D or 3D priors. It is able to animate faces driven by multiple modalities, such as images, audio, and pose codes. In order to address motion transfer for arbitrary objects, Siarohin \textit{et al.} \cite{Siarohin_2019_CVPR} propose \textit{Monkey-Net}, a framework composed of a 2D key-point detector which is jointly trained with a motion prediction and motion transfer network. Key-points are learned directly from data in an unsupervised way, while being assigned to meaningful regions of the objects, in order to enable reliable optical flow approximation for motion transfer. In the follow-up work, \textit{First Order Motion Model for Image Animation (FOMM)}~\cite{FOMM} learns a first order approximation of optical flow, based again on learnable 2D key-points, yielding very promising results. However, during cross-identity motion transfer it uses relative key-points to preserve the identity of the source, which makes the assumption that the face in the first driving frame is in the same pose with the source face. In their recent work, Wang \textit{et al.} \cite{wang2021facevid2vid} apply first order motion approximation on 3D facial key-points. This enables their so called \textit{face-vid2vid} model to perform free-view reenactment, as they can manipulate pose by rotating the 3D points. They further devise two networks, one for predicting canonical key-points and one that estimates pose and expression deformations, which enable to tackle the identity preservation problem. Unlike Wang \textit{et al.} \cite{wang2021facevid2vid}, we do not regress canonical key-points directly, but rather obtain them by removing the estimated head pose and expression deformations from regular 3D key-points, all of which are predicted from images using a unified network.

In Table~\ref{table:background} we present a detailed summary of the key features and design choices of state-of-the-art systems for reenactment and motion transfer, as discussed in this section.

\subsection{Gaze Estimation Systems}

Gaze estimation is the problem of predicting the direction that someone is looking at, based on input images or videos. Ever since~\cite{zhang15_cvpr_mpiigaze} employed CNNs to tackle the task, appearance-based methods have been the default approach. Multiple methods attempt to recover geometric features of eyes and use them to infer gaze~\cite{Park2018ETRA, Park_2018_ECCV_dpge, Wang_2018_CVPR}. Others focus on adapting generic gaze estimation networks to specific test domains, aiming to produce person-specific models~\cite{Park2019ICCV_fewshot, He_2019_ICCVW, Yu_2019_CVPR} or adapting to unseen image domains~\cite{Guo_2020_ACCV, Liu_2021_ICCV}. Recently, significant progress has been made to reduce the labeled data required to build effective gaze estimation systems, by proposing to learn gaze in unsupervised or weakly-supervised settings~\cite{Yu_2020_CVPR, Sun_2021_ICCV, Kothari_2021_CVPR}. In this work, we aim to recover gaze implicitly from the dense geometry of eyes, and employ it for driving our image generator.

\section{Methodology}\label{sec:Methodology}

Our proposed talking head synthesis system consists of three discrete networks: a network for inferring canonical key-points (Sec. \ref{subsec:canonical}), a gaze estimation network (Sec. \ref{subsec:gaze}) and an image generator (Sec. \ref{subsec:generator}). Each component of \textit{Free-HeadGAN} is trained separately on its individual task.

\subsection{Computation of Canonical Key-points}\label{subsec:canonical}

Given a portrait image, our goal here is to extract a set of key-points in a canonical space, which are independent both from the head pose and facial expressions of the subject. This representation merely depends on the geometry of the input face and therefore encodes only identity-related information. To that end, we propose a neural network $E_{can}$ that learns to a) regress sparse 3D facial key-points $\textbf{p} = \{\textbf{p}_k\}_{k=1,\dots,K}$, with $\textbf{p}_k \in [-1, 1]^3$, b) estimate head pose, as an affine transformation $\mathcal{T}=\{s, \textbf{R}, \textbf{t}\}$ and c) compute a 3D vector $\textbf{d}_k$ for each key-point $k$, that models the non-linearity of deformations caused by facial expressions. The architecture of $E_{can}$ follows that of the head pose and expression deformation estimator in \cite{wang2021facevid2vid}, with the addition of one more affine output layer that predicts $K$ points $\textbf{p}$.

Canonical key-points are obtained by first estimating the 3D points and then subtracting the expression deformations and removing translation, rotation, and scale:
\begin{equation}
\textbf{p}^{can}_k = \dfrac{1}{s} \textbf{R}^{-1}(\textbf{p}_k - \textbf{d}_k - \textbf{t}), \quad k=1,\dots,K.
\label{eq:1}
\end{equation}
We further define the inverse operation, which brings canonical points back to the original 3D space, by adding scale, rotation, translation as well as the expression deformation:
\begin{equation}
\textbf{p}_k = s \textbf{R}
\textbf{p}^{can}_k + \textbf{t} + \textbf{d}_k, \quad k=1,\dots,K.
\label{eq:2}
\end{equation}

We observed that predicting the expression-related deformations $\textbf{d}_k$ in the original 3D space, as in \cite{wang2021facevid2vid}, yields significantly better results than placing the deformation on the canonical space.

In order to train network $E_{can}$, we are given a source and a target image pair $(\textbf{x}^{s}, \textbf{x}^{t})$, which depict the same person performing a different and random pose and expression. We use $E_{can}$ to regress 3D key-points as well as pose transformations and expression perturbations from both images: $\textbf{p}^s, \mathcal{T}^s, \textbf{d}^s$ and $\textbf{p}^t, \mathcal{T}^t, \textbf{d}^t$. After that, we apply Eq.~\ref{eq:1} to get the corresponding canonical points $\textbf{p}^{s, can}$ and $\textbf{p}^{t, can}$, as shown in Fig.~\ref{fig:canonical}. Then, based on Eq.~\ref{eq:2} we aim to recover the target key-points, by transforming the canonical representation of the source using the target pose and expression, and vice versa, which results in points $\textbf{p}^{s, rec}$ and $\textbf{p}^{t, rec}$. Having access to image pairs of the same identity is crucial for guiding $E_{can}$ to focus on expression and pose discrepancies between images and thus not include identity-related information in deformation vectors $\textbf{d}^s$ and $\textbf{d}^t$.

We optimise $E_{can}$ in a supervised manner, using $K=68$ 3D facial landmarks estimated by a pre-trained RetinaFace \cite{RetinaFace} model as pseudo-ground truth data. That is, given the ground source and target landmarks $(\textbf{l}^{s}, \textbf{l}^{t})$, we minimise the distance
\begin{equation}
\mathcal{L}^p = ||\textbf{p}^s - \textbf{l}^{s}||_2^2 + ||\textbf{p}^{t} - \textbf{l}^t||_2^2,
\end{equation}
which forces $E_{can}$ to predict accurate facial key-points. We noticed that learning to estimate the 3D key-points helps $E_{can}$ to predict the perturbations caused due to expressions more efficiently. We further minimise the key-point reconstruction distance
\begin{equation}
\mathcal{L}^{rec} = ||\textbf{p}^{s, rec} - \textbf{l}^{s}|||_2^2 + ||\textbf{p}^{t, rec} - \textbf{l}^{t}||_2^2,
\end{equation}
which forces $E_{can}$ to learn the affine pose transformations as well as the deformation vectors, in an effort to recover the source and target key-points. In order to assist $E_{can}$ distinguish between rigid and non-rigid transformations, we penalise the error between the predicted target head rotation $\textbf{R}^t$ and the ground truth $\textbf{R}^{t}_{*}$ (here expressed as Euler angles). Furthermore, a regularisation term on the expression deformation vectors ensures that key-point perturbations due to expressions are kept small, as we want to avoid encoding identity-specific details in these vectors:
\begin{equation}
\begin{split}
\mathcal{L}^R = ||\textbf{R}^t - \textbf{R}^t_*||_2^2, \quad \mathcal{L}^d = ||\textbf{d}^s||_2^2 + ||\textbf{d}^t||_2^2.
\end{split}
\end{equation}
Combining the loss terms defined above, the overall objective function for network $E_{can}$ is given as
\begin{equation}
\mathcal{L}_{E_{can}} = \lambda_p \mathcal{L}^p + \lambda_{rec} \mathcal{L}^{rec} + \lambda_{R} \mathcal{L}^{R} + \lambda_{d}, \mathcal{L}^{d}
\end{equation}
with the hyper-parameters set to the following values: $\lambda_p = \lambda_{rec} = 200, \lambda_{R}=2$ and $\lambda_{d} = 5$.

\begin{figure}[!t]
\centering
\includegraphics[width=0.49\textwidth]{figures/canonical.png}
\caption{Canonical key-points computation pipeline during training. Using a pair of frames from the same person, we train $E_{can}$ to disentangle identity from expression and pose. We project the regressed 3D key-points into a canonical space and then try to reconstruct them, after swapping the canonical points of the source and target images.}
\label{fig:canonical}
\end{figure}

\subsection{Gaze estimation}\label{subsec:gaze}

\begin{figure}[!h]
\centering
\includegraphics[width=0.48\textwidth]{figures/Gaze_FreeHeadGAN.pdf}
\caption{Outline of our gaze estimation method. Having defined an eyeball template, $\mathbf{T}_{eye}$ we generate pseudo-ground truth meshes for available gaze estimation dataset and employ them to train our 3D eye mesh regression network $E_{gaze}$, which given input images produces 3D coordinates adhering to $\mathbf{T}_{eye}$.}
\label{fig:Egaze}
\end{figure}

The gaze estimation network $E_{gaze}$ learns to predict the 3D orientation of eyes from an input image of each eye. Particularly, our aim is to recover spherical coordinates $(\theta, \phi)$ that represent gaze direction and employ them for training as well as driving our reenactment system. To that end, we adopt a mesh regression approach to gaze estimation, meaning that we train $E_{gaze}$ to predict 3D meshes of eyes instead of 3D gaze vectors or angles. This approach is based on the fact that estimating dense geometry instead of few pose parameters, has recently been shown to benefit face and body pose estimation systems~\cite{Deng_2020_CVPR, Kulon_2020_CVPR, Guler2018DensePose}.

To train $E_{gaze}$, we first define a 3D eye mesh template, $\mathbf{T}_{eye}$, consisting of $N_{v}^{eye}=481$ vertices and $N_{t}^{eye}=928$ triangles, as shown in Fig.~\ref{fig:Egaze}, and enforce our model's predictions to adhere to this template so that exact correspondence exists. As common gaze estimation datasets provide gaze annotations as 3D vectors, angles or points on screen~\cite{zhang15_cvpr_mpiigaze, gaze360_2019, cvpr2016_gazecapture}, we create training and validation data compatible with our mesh regression approach by automatically fitting $\mathbf{T}_{eye}$ on available images, based on 2D sparse landmarks around the iris contour and the available gaze labels. To obtain the iris landmarks we employ the network from~\cite{Park2018ETRA}, but any similar model could have been used.

We adopt a simple architecture for $E_{gaze}$, consisting of a ResNet-34 backbone and a fully connected layer mapping eye image features to  $\mathbf{v}$, which is a vector of $3\times N_{v}^{eye}$ real values representing $N_{v}^{eye}$ 3D eye coordinates on a normalized space. We optimize $E_{gaze}$ in a supervised fashion, based on the 3D eye meshes we fitted on available gaze estimation datasets. To recover 3D points from images, we minimise the distance
\begin{equation}
\mathcal{L}^{v} = ||\mathbf{v} - \mathbf{v}^*||_1,
\label{eq:vertex_loss}
\end{equation}
between predicted coordinates $\mathbf{v}$ and the corresponding pseudo-ground truth ones $\mathbf{v}^*$. Additionally, to maintain feasible eye shape, we minimize the distance
\begin{equation}
\mathcal{L}^{e} = ||\mathbf{e} - \mathbf{e}^*||_1,
\label{eq:edge_loss}
\end{equation}
between the $3N_{t}^{eye}$ edge lengths $\mathbf{e}$ computed from $\mathbf{v}$ and those, $\mathbf{e}^*$, computed from $\mathbf{v^*}$. We define edge length to be the euclidean distance between vertices of the the same triangle according to $\mathbf{T}_{eye}$. Lastly, to boost gaze estimation accuracy we employ the gaze loss
\begin{equation}
\mathcal{L}^{g} = (180 / \pi) \arccos(\mathbf{g}^T\mathbf{g}^*),
\label{eq:edge_loss}
\end{equation}
where $\mathbf{g}$ and $\mathbf{g}^*$ are normalized 3D gaze vectors, calculated by the centers of the eye and iris, from the predicted and ground truth meshes respectively. Combining the above losses, the overall objective function for $E_{gaze}$ is given as
\begin{equation}
\mathcal{L}_{E_{gaze}} = \lambda_v\mathcal{L}^{v} + \lambda_e\mathcal{L}^{e} + \lambda_g\mathcal{L}^{g},
\label{eq:eyes_loss}
\end{equation}
with the hyper-parameters set to the following values $\lambda_v=\lambda_e=0.1$ and $\lambda_g=1$. As in our reenactment system we code gaze using angles $(\theta, \phi)$, we calculate them from predicted eye meshes using the gaze vector $\mathbf{g}=(g_x, g_y, g_z)$ as
\begin{equation}
\theta = \arctan \sqrt{g_x^2 + g_y^2} / g_z, \ \ \phi = \arctan g_y / g_z.
\label{eq:crt2sph}
\end{equation}

\subsection{Image Synthesis}\label{subsec:generator}

\begin{figure}[!b]
\centering
\includegraphics[width=0.49\textwidth]{figures/weights.png}
\caption{Illustration of optical flows and weights predicted by the flow network, in case four source images are provided instead of one (4-shot). The warped image is calculated from the source images, flows and weighs with the application of Eq.~\ref{eq:7}.}
\label{fig:weights}
\end{figure}

\begin{figure*}[!t]
\centering
\includegraphics[width=\textwidth]{figures/facesynthesis.png}
\caption{Overview of the image generation component. The generator is made up from two modules: a flow network and a rendering network. The flow network computes the optical flow for warping the reference image and features, according to the target sketch. The rendering network uses this visual information, in order to translate the target sketch into a photo-realistic image of the source. The two networks are trained jointly in an adversarial manner, with the assistance of image discriminators.}
\label{fig:facesynthesis}
\end{figure*}

We perform talking head synthesis with the assistance of an image-to-image translation network $G$, which is based on the generator of HeadGAN \cite{doukas2020headgan}, without AdaIN layers\cite{adain} as we do not processing audio features. Given a source image $\textbf{x}^s$ and a target frame $\textbf{x}^t$, along with the corresponding facial key-points $\textbf{p}^s$, $\textbf{p}^t$ and gaze angles $\textbf{g}^s$, $\textbf{g}^t$, we draw a source and a target image sketch. Gaze angles are color coded in the sketches, within the areas defined by key-points that belong to the eyes. The two sketches, together with the source image, serve as inputs to network $G$, which learns to generate a photo-realistic image $\tilde{\textbf{x}}$ of the source, in the target head pose and facial expression. In more detail, as in \cite{doukas2020headgan} network G is comprised of two modules: a flow network and a rendering network.

First, the flow network extracts visual feature maps from the source image and its corresponding key-point sketch in multiple spatial resolutions: $\{\textbf{h}^{(i)}\}_{i=1,\dots,L}$, $L=3$. Then, the target sketch is injected into the network through SPADE \cite{park2019SPADE} layers, as modulation input, and guides the prediction of the optical flow $\textbf{w}$, which warps the source image to the target expression and pose. In addition, we perform warping on each feature map, in order to align them spatially with the desired expression and pose. In practice, we utilise the backward optical flow warping operator from \textit{FlowNet 2.0} \cite{flownet2-pytorch, IMKDB17}.

The rendering network passes the target sketch through an encoder and combines the extracted feature map with the warped features $\{\bar{\textbf{h}}^{(i)} = \textbf{w}(\textbf{h}^{(i)}) \}_{i=1, \dots, L}$ and warped image $\bar{\textbf{x}} = \textbf{w}(\textbf{x}^s)$, which enter the network through SPADE \cite{park2019SPADE} layers, as modulation inputs. Up-sampling is performed with PixelShuffle \cite{pixelshuffle} layers. The final output is a photo-realistic image $\tilde{\textbf{x}}$ of the source identity, imitating the facial expressions and head pose shown in the target image.

\noindent \textbf{N-shot extension.} We further extend our method to enable few-shot learning, in cases where more than one source images are available. For that, we propose an optional attention mechanism, with the addition of one more output layer to the flow network, which now learns to compute a set of 2D weights $\textbf{m} \in \mathbb{R}^{H \times W}$, alongside the optical flow field. Given $M$ source frames $\{\textbf{x}^s_j\}_{j=1,\dots,M}$, we pass each one through the flow network, gaining flows $\{\textbf{w}_j\}_{j=1,\dots,M}$ and weights $\{\textbf{m}_j\}_{j=1,\dots,M}$. Next, the warped image is computed with the assistance of a Softmax function
\begin{equation}
    \bar{\textbf{x}} = \dfrac{\sum_j^M \exp(\textbf{m}_j) \textbf{w}_j(\textbf{x}^s_j)}{\sum_j^M \exp(\textbf{m}_j)}
    \label{eq:7}
\end{equation}
and the warped features are given as
\begin{equation}
    \bar{\textbf{h}}^{(i)} = \dfrac{\sum_j^M \exp(\textbf{m}_j) \textbf{w}_j(\textbf{h}^{(i)}_j)}{\sum_j^M \exp(\textbf{m}_j)}, \quad i=1,\dots,L.
\end{equation}
Fig. \ref{fig:weights} shows a visual example of flows and weights.

\begin{figure*}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/system.png}
\caption{End-to-end pipeline of \textit{Free-HeadGAN} during inference, for the task of reenactment. We adapt the target key-points to the facial shape (identity) of the source.}
\label{fig:system}
\end{figure*}

\noindent \textbf{Training.} The optical flow and rendering networks that make $G$ are trained jointly, on the task of self-reenactment (reconstruction). To that end, we sample the source image from the frames of the target video to obtain image pairs that belong to the same person and scene. In this case, the generated image $\tilde{\textbf{x}}$ should match the target frame $\textbf{x}^t$ that serves as ground truth. We optimise the generator G by minimising the distance between feature maps extracted from multiple layers of a pre-trained VGG network \cite{vggloss}:
\begin{equation}
   \mathcal{L}_G^{VGG} = \sum_l || VGG_l(\tilde{\textbf{x}}) - VGG_l(\textbf{x}^t) ||_1.
\end{equation}
Additionally, we apply a VGG loss on the warped image, in order to force the flow network to learn a correct flow from the source image to the desired head pose, which gives the loss term $\mathcal{L}_F^{VGG}$. We further improve the photo-realism of synthetic images by placing an adversarial loss term $\mathcal{L}_G^{adv}$ on $\tilde{\textbf{x}}$ and more specifically a Hinge GAN loss \cite{lim2017geometric}. Similarly with \cite{head2head2020} and \cite{doukas2020headgan}, we employ two critics, a general image discriminator $D_I$ and a dedicated discriminator $D_M$ for the mouth area, which are optimised alongside G. Finally, we use these discriminators to compute visual features from both real (target) and synthetic images and compute a feature matching loss $\mathcal{L}_G^{fm}$ \cite{xu2017learning} that is known to be effective at increasing the photo-realism of synthetic samples. Summing up, the overall objective function for G is given by:
\begin{equation}
\begin{split}
   \mathcal{L}_G = & \mathcal{L}_G^{adv} + \lambda_{VGG} (\mathcal{L}_G^{VGG} + \mathcal{L}_F^{VGG}) + \lambda_{FM} \mathcal{L}_G^{FM}
\end{split}
\end{equation}
where $\lambda_{VGG} = \lambda_{FM} = 10$. Both discriminators are optimised under their corresponding adversarial loss terms and have a similar architecture with the one proposed in \cite{park2019SPADE}. Please refer to Fig.~\ref{fig:facesynthesis} for an illustration of our model.

\subsection{Free-HeadGAN inference}

During inference, in the task of reenactment the source and target images belong to different identities. In order to adapt the target key-points to the facial shape of the source identity, we use $E_{can}$ to regress 3D key-points, head pose and expression deformations from $\textbf{x}^{s}$, and then evaluate Eq.~\ref{eq:1} to obtain the canonical key-points $\textbf{p}^{s, can}$. At the same time, we estimate the target pose and expression $\mathcal{T}^t, \textbf{d}^t$ from $\textbf{x}^{t}$. The adapted target key-points $\textbf{p}^{s, adapt}$ are obtained with the application of Eq.~\ref{eq:2}, which transforms the source canonical key-points using the estimated target pose and expression. In this way, we remove the target identity-related information from key-points and inject the source one, which helps to overcome to problem of identity mismatching. In parallel, we estimate eye gaze from both images with $E_{gaze}$ network and finally draw the sketches that serve as input to generator $G$, which hallucinates the output image $\tilde{\textbf{x}}$. For a visual inspection of our proposed pipeline in reenactment please refer to Fig~\ref{fig:system}.

\section{Experiments}

\subsection{Dataset and Training}
We train each component of \textit{Free-HeadGAN} independently. Both the network that infers the canonical key-points $E_{can}$ and image synthesis network $G$ (with discriminators $D_I$, $D_M$) are optimised on VoxCeleb \cite{voxceleb} video dataset, which contains over one hundred thousand clips and more than one thousand identities, at a 256 $\times$ 256 resolution. We keep the original train and test split. As a pre-processing step, we prepare three-dimensional 68 landmarks for each frame in the training split with RetinaFace model \cite{RetinaFace}, which serve as pseudo-ground truth annotations. We note that RetinaFace is pre-trained on WIDERFACE dataset \cite{yang2016wider}. For the extraction of ground truth head rotation angles, we employ a least-squares solver to determine the transformation between facial landmarks and a fixed landmark template that represents the frontal-neutral pose. The aforementioned annotations are used only for training \textit{Free-HeadGAN}. During inference, our method requires only the pair of a source and a target image.

For the optimisation of network $E_{can}$, we use ADAM solver \cite{adam} with $\beta_1 = 0.5$, $\beta_2 = 0.999$ and learning rate $\eta = 0.0002$. We use the same optimiser and hyper-parameters for training $G$ and its adversaries $D_I$, $D_M$. All models are optimised for 5 epochs on the entire VoxCeleb database. We extend \textit{Free-HeadGAN} to accommodate N-shot learning, by first training in one-shot without predicting attention weights. Then, we add the weight layer and fine-tune the GAN for one more epoch using two source images (2-shot), while freezing the parameters of the flow network.

We optimize network $E_{gaze}$ on a combination of recent gaze estimation datasets, aiming to include variation from multiple image domains. Particularly we employ ETH-XGaze~\cite{Zhang2020ETHXGaze} which includes large variation in gaze and face pose and consists of 756 thousand frames from 80 subject, Gaze360~\cite{gaze360_2019} which is captured both indoors and outdoors and includes 127 thousand training sequences from 365 subjects and MPIIGaze~\cite{zhang15_cvpr_mpiigaze} which provides over 213 thousand frames of 15 subjects, captured with laptop cameras. We employ the default training and validation sets from Gaze360 and MPIIGaze, while we perform a manual split for ETH-XGaze as gaze labels for its test data are not available. Lastly, we use ADAM optimizer \cite{adam} with $\beta_1 = 0.9$, $\beta_2 = 0.999$ and learning rate $\eta = 0.0001$.

\begin{figure*}[!ht]
\centering
\subfloat[Self-reenactment example]{\includegraphics[scale=0.26]{figures/reconstruction.png}
\label{fig:reconstruction}} \\
\subfloat[Reenactment example]{\includegraphics[scale=0.26]{figures/reenactment.png}
\label{fig:reconstruction}}
\caption{We evaluate the generative performance of \textit{Free-HeadGAN} on the tasks of (a) same-identity reconstruction (self-reenactment) and (b) cross-identity motion transfer (reenactment).}
\label{fig:reconstruction_reenactment}
\end{figure*}

\begin{table*}[!hb]
\renewcommand{\arraystretch}{1.15}
\caption{Numerical comparison with state-of-the-art methods on the tasks of self-reenactment (same-identity reconstruction) and reenactment (cross-identity motion transfer) for VoxCeleb \cite{voxceleb} test set.}
\label{table:comparison}
\begin{center}
\begin{tabular}{|c||c|c|c|c|c|c||c|c|c|c|c|}
\hline
 & \multicolumn{6}{c||}{Self-reenactment} & \multicolumn{5}{c|}{Reenactment} \\
\hline
Method & \textbf{L1} $\downarrow$ & \textbf{PSNR} $\uparrow$ & \textbf{LPIPS} $\downarrow$ & \textbf{FID} $\downarrow$ & \textbf{FVD} $\downarrow$ & \textbf{CSIM} $\uparrow$ & \textbf{FID} $\downarrow$ & \textbf{CSIM} $\uparrow$ & \textbf{ARD} $\downarrow$ & \textbf{AU-H} $\downarrow$ & \textbf{AGD} $\downarrow$ \\
\hline\hline
\textit{X2Face}~\cite{X2Face} & 13.49 & 20.69 & 0.260 & 130.2 & 697 & 0.600 & 122.1 & 0.520 & 4.39 & 0.346 & 21.4\\
\textit{fs-vid2vid}~\cite{fsvid2vid} & 17.15 & 18.52 & 0.197 & 62.8 & 471 & 0.542 & - & - & - & - & - \\
\textit{Bi-layer}*~\cite{bilayer} & 12.18 & 20.19 & 0.152 & 92.2 & 394 & 0.590 & 172.8 & 0.563 & \textbf{1.01} & \textbf{0.296} & 16.6 \\
\textit{FOMM-abs}~\cite{FOMM} & 12.34 & 20.93 & 0.153 & 64.9 & 338 & 0.754 & 100.7 & 0.587 & 1.46 & 0.298 & 13.8 \\
\textit{FOMM-rel}~\cite{FOMM} & - & - & - & - & - & - & 63.7 & 0.765 & 12.53 & 0.400 & 21.2 \\
\textit{HeadGAN}~\cite{doukas2020headgan} & 11.32 & 21.46 & 0.112 & 36.1 & 254 & 0.807 & 58.0 & 0.688 & 1.35 & 0.326 & 16.8 \\
\textit{Free-HeadGAN} & \textbf{9.96} & \textbf{22.16} & \textbf{0.100} & \textbf{35.4} & \textbf{248} & \textbf{0.810} & \textbf{53.9} & \textbf{0.789} & 1.26 & 0.351 & \textbf{13.3} \\
\hline
\end{tabular}
\end{center}
\end{table*}

\subsection{Evaluation Metrics}

We evaluate the reconstruction capabilities of our method quantitatively, using L1 distance (L1), Peak signal-to-noise ratio (PSNR) and Learned Perceptual Image Patch Similarity (LPIPS) \cite{lpips}. All three metrics measure the distance, between the synthesised and ground truth target frames. L1 distance is computed across RGB channels that are in the range [0, 255]. PSNR is the ratio between the maximum possible power of a signal and the power of noise that affects its correctness. It is defined as $20 \cdot \log_{10} (\mathit{MAX}_I) - 10 \cdot \log_{10} \mathit{MSE}$, where $\mathit{MAX}_I=255$ and $\mathit{MSE}$ denotes the mean squared distance. LPIPS \cite{lpips} is another widely used metric to measure the fidelity of reconstruction, which uses a pre-trained AlexNet model for the extraction of a feature maps. The similarity score between two images is calculated as the distance of their visual features.

Furthermore, we assess the photo-realism of generated images with Fréchet Inception Distance (FID) \cite{NIPS2017_8a1d6947, Seitzer2020FID} and Fréchet Video Distance (FVD)~\cite{unterthiner2018towards}, as we handle video data and therefore crucial to measure the performance of models considering the temporal coherence among frames.

The identity preservation in the synthesised data is calculated with Cosine Similarity (CSIM) between embedding vectors from the target and corresponding generated images. All embeddings are computed with the assistance of ArcFace \cite{deng2018arcface}. For reenactment, where we have no access to ground truth data, we extract the embedding from the source image(s) and compare it with the embeddings coming from generated data. This leads to lower CSIM values, as the source and target pose do not usually match and ArcFace's output is slightly affected by poses.

We use Action Units Hamming distance (AU-H) to measure expression transferability of models. OpenFace \cite{openface} with \cite{AUs}, runs on target and synthetic images for the detection of Action Units (AU). OpenFace recognises whether or not a set of AUs is present in a facial image, with the prediction of an AU boolean vector. We measure the Hamming distance between boolean vectors that are extracted from the corresponding target and synthetic data.

We measure the correctness of pose transfer with Average Rotation Distance (ARD), which is the $l1$-distance of Euler angles that correspond to head pose, between the target and generated frames in degrees.

Finally, we evaluate gaze transfer with Average Gaze Distance (AGD). Given the gaze vector $\textbf{g}^t$ regressed with $E_{gaze}$ module from the target frame, and the vector $\tilde{\textbf{g}}$ extracted from the corresponding synthetic image, we compute the angle between vectors as $(180 / \pi) \arccos{(\tilde{\textbf{g}}^{\top} \textbf{g}^t)}$ and average across frames to obtain AGD in degrees.

\subsection{Comparison with State-of-the-Art} \label{sec:comparison}

We compare our one-shot \textit{Free-HeadGAN} system both numerically and visually with state-of-the-art methods under two setups: a) same-identity reconstruction or self-reenactment, where the source and target identities coincide, and b) cross-identity motion transfer or reenactment, in which source and target identities are different. Please refer to Fig.~\ref{fig:reconstruction_reenactment} for a visualisation of the two tasks. More specifically, we compare our approach with \textit{X2Face}~\cite{X2Face}, \textit{few-shot vid2vid (fs-vid2vid)}~\cite{fsvid2vid}, \textit{Bi-layer Neural Avatars (Bi-layer)}~\cite{bilayer}, \textit{First Order Motion Model (FOMM)}~\cite{FOMM} and \textit{HeadGAN}~\cite{doukas2020headgan}. Please note that we tested two variations of \textit{FOMM}, one with absolute (\textit{FOMM-abs}) and one with relative key-point coordinates (\textit{FOMM-rel}), which is the default setting for cross-identity motion transfer. For \textit{X2Face}~\cite{X2Face}, \textit{FOMM}~\cite{FOMM} and \textit{HeadGAN}~\cite{doukas2020headgan}, we use the models provided by their authors, all trained on VoxCeleb~\cite{voxceleb} dataset. For \textit{Bi-layer} method, we used the network parameters provided by its authors, trained on VoxCeleb2 dataset* \cite{Chung18b}. We trained \textit{fs-vid2vid}~\cite{fsvid2vid} using the official open source implementation, as pre-trained models are available. Given that the source code for \textit{MarioNETte} \cite{MarioNETte:AAAI2020}, \textit{Warp-guided GANs}~\cite{Geng2018WarpguidedGF} and \textit{face-vid2vid}~\cite{wang2021facevid2vid} is not publicly available, we were not able to measure \textit{Free-HeadGAN}'s performance against these systems.
\begin{figure*}[!ht]
\centering
\begin{picture}(520,330)
\put(0,10){\includegraphics[width=0.98\textwidth]{figures/comparison_rec.png}}
\put(20,0){source}
\put(83,0){target}
\put(135,0){\textit{X2Face}~\cite{X2Face}}
\put(194,0){\textit{fs-vid2vid}~\cite{fsvid2vid}}
\put(262,0){\textit{Bi-layer}~\cite{bilayer}}
\put(325,0){\textit{FOMM}~\cite{FOMM}}
\put(384,0){\textit{HeadGAN}~\cite{doukas2020headgan}}
\put(446,0){\textit{Free-HeadGAN}}
\end{picture}
\caption{Visual comparison of our method with baselines on the task of self-reenactment.}
\label{fig:comparison_rec}
\end{figure*}
\begin{figure*}[!hb]
\centering
\begin{picture}(520,300)
\put(0,10){\includegraphics[width=0.98\textwidth]{figures/comparison_reen.png}}
\put(20,0){source}
\put(83,0){target}
\put(135,0){\textit{X2Face}~\cite{X2Face}}
\put(198,0){\textit{Bi-layer}~\cite{bilayer}}
\put(252,0){\textit{FOMM-abs}~\cite{FOMM}}
\put(319,0){\textit{FOMM-rel}~\cite{FOMM}}
\put(384,0){\textit{HeadGAN}~\cite{doukas2020headgan}}
\put(446,0){\textit{Free-HeadGAN}}
\end{picture}
\caption{Qualitative evaluation of our method against baselines on the task of reenactment.}
\label{fig:comparison_reen}
\end{figure*}

In Table~\ref{table:comparison}, we present a quantitative comparison of the proposed \textit{Free-HeadGAN} with the aforementioned baselines. For self-reenactment, we reconstruct the entire test set of VoxCeleb, while we have chosen 15 pairs of reference images and driving videos for reenactment, the same as in \cite{doukas2020headgan}. As suggested by the results, our method creates superior samples, with respect to image quality and reconstruction fidelity, both in same-identity reconstruction and cross-identity motion transfer. In addition, our approach  preserves the identity of the source better in all experiments. For expression transferability, we observe that \textit{Free-HeadGAN} is left slightly behind from \cite{bilayer} and \cite{doukas2020headgan}. This is attributed to the following two reasons: First, the ground truth 3D facial landmarks annotations have been extracted with \textit{RetinaFace} \cite{RetinaFace}, which is not a model focusing exclusively on landmark prediction and occasionally misses fine details. Second, during reenactment the adaptation of target key-points to the source identity with network $E_{can}$ relies on the regression of expression deformations, which also comes with small inaccuracies. This is compromise we make, as we have chosen 3D facial key-points instead of 2D and we pay particular attention to identity preservation of the source with network $E_{can}$.

In Fig.~\ref{fig:comparison_rec} and Fig.~\ref{fig:comparison_reen} we show examples from a visual comparison of \textit{Free-HeadGAN} with the baselines, on the task of reconstruction and reenactment respectively. As can be seen, the qualitative results confirm our numerical analysis. More specifically, our proposed system outperforms all previous state-of-the-art methods in terms of reconstruction fidelity, photo-realism, identity conservation and gaze transfer. The generated samples of \textit{HeadGAN} \cite{doukas2020headgan} are comparable to ours in terms of visual quality. Nonetheless, our method appears to be significantly more reliable on eye gaze transfer than \textit{HeadGAN}, thanks to our gaze prediction network and explicit gaze conditioning for image synthesis. We urge the reader to refer to our supplementary video for a better inspection of our synthesised data.

\subsection{Ablation Study}

\begin{table}[!b]
\renewcommand{\arraystretch}{1.15}
\caption{Evaluation of \textit{Free-HeadGAN} N-shot extension, in case more than one source images are available.}
\label{table:N-shot}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|}
\hline
N-shot & \textbf{L1} $\downarrow$ & \textbf{PSNR} $\uparrow$ & \textbf{LPIPS} $\downarrow$ & \textbf{FID} $\downarrow$ & \textbf{FVD} $\downarrow$ & \textbf{CSIM} $\uparrow$ \\
\hline
1-shot & 9.96 & 22.16 & 0.100 & 35.4 & 248 & 0.810 \\
2-shot & 9.02 & 22.99 & 0.088 & 32.7 & 224 & 0.834 \\
4-shot & 7.90 & 24.01 & 0.077 & 30.7 & 207 & 0.857 \\
8-shot & 7.20 & 24.96 & 0.068 & 29.9 & 196 & 0.868 \\
\hline
\end{tabular}
\end{table}

\begin{figure}[!h]
\centering
\begin{picture}(260,140)
\put(0,10){\includegraphics[width=0.49\textwidth]{figures/N-shot.png}}
\put(18,0){1-shot}
\put(82,0){2-shot}
\put(147,0){4-shot}
\put(210,0){8-shot}
\end{picture}
\caption{Synthetic examples when using one, two, four or eight source images in the N-shot scenario. The results improve in terms of image quality as we get access to more source inputs.}
\label{fig:shot}
\end{figure}

\begin{table}[!b]
\renewcommand{\arraystretch}{1.15}
\caption{Ablation study results on the significance of \textit{Free-HeadGAN} components on the task of reenactment.}
\label{table:ablation}
\centering
\begin{tabular}{|c||c|c|c|c|}
\hline
Variation & \textbf{FID} $\downarrow$ & \textbf{CSIM} $\uparrow$ & \textbf{AU-H} $\downarrow$ & \textbf{AGD} $\downarrow$ \\
\hline
\textit{Free-HeadGAN} & \textbf{53.9} & \textbf{0.789} & 0.351 & \textbf{13.3} \\
\textit{Free-HeadGAN} w/o $E_{can}$ & 66.6 & 0.640 & 0.304 & - \\
\textit{Free-HeadGAN} w/o $E_{gaze}$ & - & - & - & 17.7 \\
\hline
\end{tabular}
\end{table}

We follow a few-shot learning approach to adjust our system in a setting where multiple source images are available. Although our flow network extended to predict weights has been trained in a 2-shot scenario, during inference it can operate for any number of source frames $N$. We examine the setting where $N=2,4$ or 8 source frames are given and report the results on the task of reconstruction of VoxCeleb test set, in Table~\ref{table:N-shot}. As suggested by all evaluation metrics, the generative performance of our system improves when the number of available images increases. In Fig.~\ref{fig:shot} we present visual examples that show the effect of the number of source images $N$ in the quality of synthesised data. The results clearly demonstrate the beneficial effect of N-shot learning.

Next, we examine the significance of the two front-end components of our talking head synthesis system and more specifically networks $E_{can}$ and $E_{gaze}$.

We focus on cross-identity motion transfer, as the canonical key-point estimator $E_{can}$ is particularly developed to tackle the identity mismatches in reenactment, since it allows to adapt the target key-points to the facial shape of the source. In order to evaluate its contribution, we develop a variation of \textit{Free-HeadGAN} by replacing $E_{can}$ network with \textit{RetinaFace} \cite{RetinaFace}. That is, we utilise directly the 3D key-points regressed by \textit{RetinaFace} in order to draw the sketches that serve as conditional input to the generator, without adapting them to the identity of the source. The quantitative results displayed in Table~\ref{table:ablation} indicate that removing $E_{can}$ has a critical effect on CSIM metric that measures identity preservation. Moreover, the overall quality of samples degrades as FID score increases. On the contrary, expression transfer slightly improves, indicating that the canonical space key-points actually retain a small amount of expression information.

In order to validate the importance of explicit eye gaze conditioning in image synthesis, we test a second variation of our system, where we do not encode any gaze information into key-point sketches. To that end, we train a \textit{Free-HeadGAN} generator that learns to transfer gaze direction relying solely on the facial key-points that belong to the eyes, as we do not color code the gaze angles inside the eye cavities. We evaluate the performance of this variation with AGD metric, which measures the average angular distance between the driving and generated gaze on the test set for reenactment. We display AGD results in Table~\ref{table:ablation}, which confirms the significance of gaze estimator $E_{gaze}$ quantitatively. Additionally, in Fig.~\ref{fig:gaze} we illustrate some cases where the model variation without explicit gaze input fails to capture the target eyes direction.

\begin{figure}[!t]
\centering
\begin{picture}(260,185)
\put(0,20){
\includegraphics[width=0.48\textwidth]{figures/gaze.png}}
\put(30,10){target}
\put(100,10){\textit{Free-HeadGAN}}
\put(185,10){\textit{Free-HeadGAN}}
\put(195,0){w/o $E_{gaze}$}
\end{picture}
\caption{Examples where explicit gaze control is essential for successful gaze transfer, as 68 facial landmarks appear to be insufficient.}
\label{fig:gaze}
\end{figure}

Lastly, we quantitatively verify the benefit of implicitly inferring gaze from predicted dense 3D eye coordinates instead of learning to directly predict few gaze parameters. To that end, we train two versions of $E_{gaze}$ one predicting dense eye meshes as described in Sec.~\ref{subsec:gaze}, $E_{gaze}^{meshes}$, and one predicting 3D gaze vectors, $E_{gaze}^{vectors}$. We perform within-dataset, cross-subject experiments on the recent gaze estimation datasets MPIIGaze~\cite{zhang15_cvpr_mpiigaze}, Columbia~\cite{CAVE_Columbia2013}, UTMV~\cite{sugano2014utmv}, and Gaze360~\cite{gaze360_2019}. We compute the AGD and present results in Table~\ref{table:gaze}, which confirms our initial suggestion.

\begin{table}[!h]
    \setlength{\tabcolsep}{6pt}
    \renewcommand{\arraystretch}{1.15}

    \centering
    \caption{Comparison between the 3D mesh and 3D vector regression approaches to gaze estimation, on within-dataset, cross-subject experiments.}
    \label{table:gaze}
    \begin{tabular}{|c||c|c|c|c|}
    \hline
     & \multicolumn{4}{c|}{\textbf{AGD} $\downarrow$} \\
    \hline
    Variation & MPIIGaze & Columbia & UTMV & Gaze360 \\
    \hline
    $E_{gaze}^{vectors}$ & 4.83 & 3.84 & 5.62 & 12.7 \\
    $E_{gaze}^{meshes}$ & 3.35 & 1.12 & 4.11 & 10.4 \\
    \hline
    \end{tabular}
\end{table}

\subsection{Pose and Gaze Manipulation}

Apart from cross-identity motion transfer (reenactment) and facial video compression and reconstruction (self-reenactment) we can use \textit{Free-HeadGAN} to edit portrait images. That is, we can set the driving head pose manually, simply by rotating the 3D source key-points. Moreover, we can change the gazing direction of the reference subject, by editing the estimated gaze angles before feeding them to the generator through the target sketch. In Fig.~\ref{fig:edit} we demonstrate the ability of our proposed system to edit the head pose and eye gaze of any given reference image. Here, the left column shows the original reference image, while the next three columns depict synthesised images  by our method, after editing pose and gaze. Our image editing task makes \textit{Free-HeadGAN} a powerful tool for data augmentation, as our method can replace the naive and widely-used affine transformations on image data with complex non-linear transformations of human heads. Moreover, considering that our system provides strong identity preservation, such image augmentation can benefit various computer vision tasks related to face recognition.

\begin{figure}[!t]
\centering
\includegraphics[width=0.485\textwidth]{figures/edit.png}
\caption{The 3D key-point modeling of faces enables our system to perform free-view synthesis, as we are able to manually edit the head pose of a given reference image. Furthermore, we have explicit gaze control over the subject.}
\label{fig:edit}
\end{figure}

\section{Limitations}

Although our method performs one-shot reenactment with unparalleled image quality and photo-realism while proving explicit gaze control and strong identity preservation properties, it does not come without any limitations. Considering that high quality data are crucial for deep learning algorithms, the same applies to our system. In particular, \textit{Free-HeadGAN}'s performance in extreme target poses, such as large head rotations, highly depends on the quite limited pose distribution in open source databases such as \cite{voxceleb}. Moreover, as suggested by our qualitative analysis and FID scores \cite{NIPS2017_8a1d6947}, there exists a significant performance gap between self-reenactment and reenactment. Aside from the adaptation of key-points in cross-identity motion transfer, this gap is caused by the random selection of source and target image pairs during training and could be improved by a more sophisticated learning strategy, where progressively tougher pairs are chosen.
Last but not least, even though our method does not rely on 3DMMs \cite{3dmms, 3dmmsbooth, Booth, Booth_2016_CVPR}, the supervision of our networks depends on the quality of 3D landmark pseudo annotations with \textit{RetinaFace} \cite{RetinaFace} and labeled gaze data, which come with their own inaccuracies.

\section{Conclusion}

We presented \textit{Free-HeadGAN}, a model that extends \textit{HeadGAN}~\cite{doukas2020headgan} while releasing it from 3DMM fitting and 3D face rendering requirements in pre-processing. Instead, we condition synthesis on 3D landmarks which also support free-view synthesis. We designed a network that takes care of the identity mismatches in cross-identity motion transfer. Finally, we proposed a gaze estimation module and demonstrated the advantage on utilising gaze signals for precisely controlling of eye movements of the generated samples.

redpajama_set_name: RedPajamaArXiv


Mountain biking, whitewater rafting on the Animas River, hiking through fields of wildflowers — the opportunities to uncover Colorado's unspoiled wilderness in Durango are endless. Go fishing at 7,800 feet in the sapphire-blue waters of Vallecito Lake or immerse yourself in the rugged beauty of the San Juans on horseback. During the winter, more than 92 ski runs, five terrain parks and 1,525 acres of skiable terrain await at family-friendly Purgatory Resort. Other cold-weather activities include snowmobiling, dogsledding and snowshoeing.


The Local Government Association (LGA) warned that the scheme has been underfunded for years by central government, and is being financially topped up by councils at the expense of other discretionary services.
In a new report – due to be published later this week – the LGA is calling on the Government to fully fund the concessionary fares scheme and give councils control over the Bus Service Operators' Grant.
Cllr Martin Tett, LGA transport spokesman, said: 'Faced with significant funding pressures, many across the country are being forced into taking difficult decisions to scale back services and review subsidised routes.
New figures show that the number of bus journeys made in in England has fallen to its lowest level in a decade, according to official figures.
There were 75 million fewer bus journeys made in 2016/17 compared to the previous year, a decrease of 1.7%.
This article first appeared on localgov.co.uk.


text:
Postdoctoral Positions Available
Icahn School of Medicine at Mount Sinai, New York City
So you earned your doctoral degree, what's next for you?
We have postdoctoral positions available at the Department of Environmental Medicine and Public Health, the Icahn School of Medicine at Mount Sinai in New York City.
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Ctrl +c and Backspace on Google Chrome I have an application in which I am listening for KeyPress events in an iFrame. On Google Chrome and IE, CTRL+C and backspace never trigger the key press event. Backspace takes me to the previous page and CTRL+C is not captured at all.
Please let me know if there's any existing solution for this.

A: I was listening on Keypress events, it turns out that on Chrome some of the events trigger through Keydown or Keyup events.

redpajama_set_name: RedPajamaStackExchange


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text:
Honda (ARTA) and Ralph Firman parts company in Super-GT
According to Autosport Japan magazine, Honda via ARTA have decided to part company with english racer and ex-F1 driver Ralph Firman. Firman have been with the ARTA team racing in the GT-500 class of Japan's Super GT championship for many years, winning at least one driver & manufacturer title and delivering countless race wins and pole positins. Also confirmed to be leaving ARTA is team-mate Yuji Ide, this time confirmed via his personal homepage at http://www.yuji-ide.com.
Note that at this time however, news of Ralph Firman's leaving is only from Autosport and Honda has not yet issued an official press release confirming his departure. As the Super GT championship nears its start later this year, Honda will issue press statements detailing their challenge - teams using the new HSV 010GT race car, as well as drivers and team personnel. We will publish the official announcements when that becomes available.
In the meantime, Team Kunimitsu have already announced their intention to continue with their drivers lineup of Takuya Izawa and Naoki Yamamoto during the Tokyo Auto Salon on January 15th last week. This has also been posted on the official Super GT website.
Posted by WongKN at 7:53 PM 0 comments
JDM Honda Fit Shuttle (dealer update)
Just came back from the local dealer and saw the dealer pre-release info.
Pics like the one bellow came from the salesmen training brochure.
After having a close look to both brochures and speaking with the dealer, here is what I can tell you :
Contrary to some early rumours, it's not a 7 seater, but simply a 5 seater Fit Wagon. The front seems to come straight from the current US version (20cm longer) and the rear has been extended about 30cm to make room for a larger trunk, which becomes class leading in Japan when compared with other compact wagons like the Corolla.
Inside expect a Fit except from higher trim details and options. According to the dealer Honda expects some people driving larger vans and wagons to downsize to the Shuttle, which is why special attention has been put over road noise, seating fabrics (which get a suede/leather combo standard on all models), etc.
Outside you can see it in the pics, though those have been taken from a phone, probably not with the agreement of the dealer, and they are a bit distorted.
Engines are either the 1.5L CVT or 1.3L CVT Hybrid (i.e. exactly the same as in the Fit but without the most popular 1.3L version). Right now I can't remember exactly but I think there was also a 1.5L 4WD 5AT.
Rated FE is the same as in the Fit, i.e. 30km/L on the hybrid and 20km/L on the 1.5L FF gasoline. Note that those figures are Japanese 10-15 mode and not to be compared with anything EPA/EUDC stuff. As I said, rated figures are identical to the regular Fit's.
As predicted by the early rumours, debut is said to be around March.
Price to sit between the Fit and the CR-Z, which is not much to say. Personally, I wouldn't expect to pay more than 200k over the Fit, i.e. a bit less than for an Insight.
Other than that, I'm intrigued by the USDM long front; does it mean there is any chance Honda will bring it to the US? If not, why make it longer and lose some maneuverability; just a matter of styling?
Source : Japanese Honda dealer
Posted by danielgr at 12:39 PM 0 comments
StepWGN is Japan's 2010 best selling van (8 seats)
Honda finally did it and, 3 generations after the original StepWGN redefined the segment in 1996, the new StepWGN has climbed to the top of the sales charts once again.
After a very close battle for the mid-year crown, the gap with the 2nd placed Nissan Serena increased to about 6000 units despite the Serena's November FMC, ending 80.934 vs. 75.040 for 2010. That was good enough for an overall 7th place in the Japanese market (excluding k-cars).
Interestingly, both the new Toyota Voxy and Serena beat the StepWGN's fuel economy (which was best in class when it reached the market in 2009), but it seems that consumers are looking beyond that this time around. I wonder though if it'll be able to retain the lead for a second year (it was the best selling van in December but that was a first since September).
As predicted though, the StepWGN couldn't match its smaller Freed brother, who ended up on a respectable 5th place at 95.123, helped by the recently introduced Spike variant. In my opinion, the rumored hybrid scheduled for this fall could give it the strength to fight for a top4 in 2011.
For those having more interest on Japanese market, here is the rest of the top 5.
Market leader was of course the Prius by a wide margin (315.669), and having broken Corolla's two decades old record.
Second was to the Fit (185.439), which is closing on the Prius since the government green cash-back campaign ended in September, but still can't reach Toyota's all mighty rival. Some analysts say that is unlikely to happen until the end of the green tax-incentives that remain in place until 2012.
Third was Toyota's Fit fighter, the Vitz/Yaris (122.248), and fourth was for the fleet-friendly Corolla that sells in so many different variants is difficult to keep track of them (111.265).
Sources : Honda PR, Japan Automobile Dealers Association.
Posted by danielgr at 9:31 PM 0 comments
Labels: 2011 honda, 8-seater, honda, minivan, StepWGN
Latest rumours for Malaysia
Just back from the annual Honda Media gathering event. Some interesting news and rumours. In the presentation delivered in conjunction with the CEO's speech, amongst the current models shown, very briefly the CR-Z also flashed on the screen.
In last year's event, it was the Freed which was very briefly shown. Then I knew Honda Malaysia intended to launch it (the Freed) last year which turns out to be true. So following 'history', the photo of the CR-Z shown this year means Honda Malaysia is working on bringing the CR-Z here. It is most logical and somewhat unavoidable actually, as eventhough the FD2 Civic Type-R is still officially on sale, it was 'NOS', meaning whatever is left of the unsold units Honda Malaysia brought in last year before production was actually stopped in October. So the FD2R will very quickly dissapear from the local line-up. This means a 'sporty model' is needed to replace the FD2R and the only model which meets this criteria in any form today is the CR-Z.
The original plan was to actually launch it in 2012, mainly due to the inability of the Japan factory to meet demands: i.e. it is true that for the CR-Z, demand outstrips supply. But because of the extra-ordinary incentive offered by the Malaysian government (where practically all taxes are dropped for hybrid cars) and which will only last till the end of this year, just like the Insight, there is now a renewed sense of urgency to get approval to launch the CR-Z in Malaysia. Again, much depends on the ability of the Japan factory to meet the demand.
Also highlighted in the presentation was that to-date, almost 1,500 Insights have been officially sold. I.e. confirmed bookings have been received for 1,500 units but of course, none has been delivered so far as stocks is still on the way over here. The intention by Honda Malaysia is to start delivery of the first units by next month, a few weeks ahead of original schedule.
Some bad news. Regardless of whether there will be a 9Gen Civic Type-R, Malaysia will not be able to get it anymore. It seems that the FD2R was a very special exception, given because Honda's brand image needs an urgent boost here in Malaysia. So the FD2R was approved for sale here. My understanding is several countries in Asia and outside have tried to get approval as well, and all have been rejected. This must have caused some amount of friction internally as questions must have obviously been asked why a small country like Malaysia can get approval but not the larger countries which can commit to a much larger sales target. Well, that puts paid to whatever chances Malaysia have got for the 9Gen CTR. This also means that whoever owns an FD2R, he or she should really treasure it because it is indeed a very unique, once a lifetime opportunity to own such a gem.
Finally, it was officially confirmed that the 9Gen Civic for Asia (including Japan) has been delayed. Until when, I am not at a liberty to tell. But rumours that the delay was because the current outgoing 8Gen is such a well balanced package that Honda is having a hard time ensuring the 9Gen will meet high expectations, is indeed true. As the sedan market in Japan has been shrinking steadily, so the Civic is no longer that critical a model that it used to be (which is sad because of all models, the Civic is considered the heart and soul of Honda). So a delayed launch is acceptable now. And this means a delay for Asia as well.
The 9Gen concept Civic for the U.S. and Canada has already been shown in Detroit and it will actually be launched in a couple of months time, followed closely by the Civic Hybrid a few months later. For Asia, it seems that it is still some time away.
Posted by WongKN at 12:21 AM 0 comments
Honda Exhibition at Tokyo Auto Salon 2011
January 12, 2011 - Honda Motor Co., Ltd. announced that it will present an exhibit at Tokyo Auto Salon 2011 with NAPAC, to be held from Friday, January 14, to Sunday, January 16, 2011, at Makuhari Messe in Chiba, Japan. On display will be automobiles and motorcycles (including display vehicles), power products, racing vehicles, and other products, that highlight the many possibilities that customization has to offer while simultaneously conveying the joy of mobility and motorsports appeal.
At this year's event, Honda Access Corporation, the manufacturer of genuine Honda aftermarket parts and accessories, will display a TS-1X test study model based on the CR-Z.
Also on display are accessorized production automobiles and motorcycles, along with a variety of specially designed vehicles to show the playful nature of customization, such as the Freed Spike caravan car, the Acty Truck and the Enepo generator special coloring editions.
Racing vehicles on display will include the HSV-010 GT machine, which won both the driver and team titles for the Super GT Series GT500-class races, and the RC212V, which competed in the MotoGP class races, the pinnacle of world motorcycle road racing competition.
9Gen Civic Concept Debuts in the U.S.
A.H.M. (American Honda Motors) has debuted the new 9th Generation Honda Civic Sedan and Coupe concepts at the Detroit Motor show now running. We are covering this event first-hand via our main (Temple Of VTEC) site which is focussed on the U.S. and Canada markets. For more information and more photos, including our own photos (like the above), please visit our news article via the link below
http://www.vtec.net/news/news-item?news_item_id=950281
For the million dollar question now on probably every Honda enthusiasts' lips in Asia, this is not 'exactly' what we will see. The 9Gen U.S. Civic will debut in the U.S. and Canada this quarter itself. The Euro version (which is very different) will follow suit soon. But once again, the JDM version will be a different version from the U.S. and Euro. My understanding is that it will not debut so soon. I suspect (this is a personal guess) the 9Gen JDM Civic will launch probably during in the Tokyo Motor Show which should be run around end of this year. This makes its debut around half a year later than the U.S. and Euro debut. My understanding also is that the Asian Civic will once again be based on the JDM version. So once again, we have to wait for HRT in Thailand to develop the 'approved' Asian edition, then for HAT (Honda Automobile Thailand) to launch it, before we can expect to see it in the rest of Asia.
At this moment in time, I do not have or is not able to discuss more details about the car. I was given an assurance that there WILL be a Civic Hybrid which, as proven by the U.S. version, will use the new Lithium Ion (LiOn) batteries which has been developed by the Honda-Yuasa joint venture. As expected, I could not get any committed answer whether the 9Gen will also see a Civic Type-R or not.
Expect to see the 9Gen Asian Civic end of this year or early next year. This however is just my own personal estimate.
Posted by WongKN at 11:49 PM 2 comments
New Fit variant for Japan : Fit Shuttle
News of a new variant of Honda's most popular model, the Fit, known as the Fit-Shuttle is now everywhere in japanese car magazines.
While not yet confirmed in Honda's own site at http://www.honda.co.jp, this new variant is FOR REAL. It is a 7-seater compact station wagon in configuration, similar to the Honda AIRWAVE in the previous generation line-up of the JDM Honda Fit (though the AIRWAVE was more a 5-seater station wagon in configuration).
Key components of this new Fit-Shuttle, the design, power-train and so forth as derived from the Fit as with the Airwave. The leaked brochure pages shows that there will be three main types available, a 1.3l, a 1.5l, and a HYBRID. Expect similar if not almost identical powertrain and transmission specifications as the JDM Fit.
Labels: 2011 honda, 7-seater, Fit, fit shuttle, fit wagon
The FN2R in Malaysia ?
In the recent KLIMS 2010 last month, one of the exhibits was a red FN2R. According to one of the HM staff I chatted with, the FN2R was exhibited in the KLIMS for two primary reason. Firstly they wanted to highlight the continued commitment to sporty models in Honda's line-up. It is no secret that many Honda die-hards are frustrated with the lack of outright sporty models in Honda's current line-up. The fact that Honda discontinued production of the highly regarded FD2R met especially great reactions from the fans.
In the KLIMS, Honda Malaysia displayed the new hybrid CR-Z 2-door sports coupe as its primary showcase. Meanwhile quietly sitting in a corner is the red FN2R. Does that indicate the HM is planning to sell this model in Malaysia ? The logical implication is that to continue to maintain its sporty reputation, with the FD2R gone, the FN2R would be the replacement.
The work needed to get permission to sell the FD2R here was tremendous. The truth is Malaysia is such a small market that it is difficult to justify the sale of any niche model which a Type-R definitely is. But with the great benefit from that work already in place, it would be a waste to just let the FD2R disappear and HM be left without a true sports model yet again.
But the other angle is that if HM wants to sell the FN2R here, they have to re-do most of the work they did with the FD2R. For this, it is important to bear in mind that the 8Gen Civic is nearing its end-of-life and for Europe, the new 9Gen Civic will be launching very soon. Does it make sense to spend a lot of effort to launch what is essentially an previous generation model for sale here and only a few months (obviously units must be imported from Europe but they won't be manufacturing it anymore very soon).
So the logical choice would be that if any work is to be done to justify for a replacement for the Fd2R, that replacement must be the hybrid CR-Z.
So, why then did HM display the FN2R ? According to what I was told, with the FD2R no longer available, for this year's MME Honda Malaysia intend to field an FN2R. So this then seems to be the REAL reason for that FN2R in the Honda booth in KLIMS 2010.
10 Days, 500 bookings
Honda Malaysia Sdn. Bhd. announced the All-New Insight has now received over 500 confirmed bookings after only 10 days from the official launch. This marks a significant milestone as it is the highest ever achieved confirmed bookings of a hybrid vehicle in Malaysia, within such a short period of time.
In 2009, the total industry volume (TIV) was 505,509 units. Out of that, total hybrid cars sold was just a mere 0.06% which accounts for 297 vehicles. Today, the confirmed bookings of the All-New Insight alone have already exceeded the total hybrid vehicles sales in 2009 by 40%. This number of confirmed bookings is a 12.5% achievement of its 2011 target of 4,000 units.
As part of Honda's continuous effort to strive for Customer Satisfaction No. 1, Honda Malaysia will expedite to complete the delivery of the 500 bookings within a period of one month. Delivery will start from 15 February 2011.
The All-New Insight is an entry hybrid model priced at RM98,000.00. To further encourage the use of hybrid vehicles here, Honda Malaysia is extending the warranty of the All-New Insight's battery for another 2 years, on top of the 3-year warranty offered by Honda Motor, Japan. The 5-year or 140,000 km warranty, depending on which comes first, is Honda's commitment to promote the use of hybrid technology and to provide better customer satisfaction. Furthermore, Honda Malaysia will be reducing the price of the All-New Insight spare parts, especially its battery by more than 40%.
In Malaysia, the All-New Insight is targeted at eco-oriented individuals who believe in doing their part to protect the earth for future generations. It is also intended for independent and image conscious young professionals who are aware of eco issues, and would consider using environmentally friendly products if they offer great value with practicality and style.
For more information on the All-New Insight, please call Honda Toll Free number at 1-800-88-2020, or log on to www.honda.com.my
New CEO for Honda Malaysia
Honda Malaysia announced the appointment of a new Managing Director/Chief Executive Officer earlier this week. Yoichiro Ueno-san has been announced as the new M.D./C.E.O. replacing outgoing Toru Takahashi-san.
Ueno-san, 47, was previously the Assistant Vice President of Automobile Sales of American Honda Motor Co. Inc., where he was involved in the overall planning and coordination of national sales and marketing in the US market. He brought with him over 20 years of working experience in Honda's automobile business and a wealth of knowledge in the global automotive industry, having served markets outside Japan like New Zealand and Thailand, and worked with overseas division and countries such as Asia & Oceania, China and North America.
When he was with American Honda Ueno-san worked extensively with their huge network of dealers. "Although I was involved in strategizing the sales and marketing operation on the national level, I had a lot of opportunities to visit dealers in various states, which helped me understand the different business circumstances in each area, find out the common issues and propose the best plan to be executed on the nationwide scale. I was also deeply involved in improving inventory efficiency, such as sales and production planning, minimizing the inventory balance amongst dealers and enhancing logistic efficiency.", he said.
Commenting on his transition from the US to Malaysia market, Ueno said: "US is a large country and it has the biggest sales volume of Honda cars worldwide. In order to manage the big volume and the large number of dealers, American Honda has a very advanced system and logistic network. However, the process in which every customer makes a purchase at a Honda dealership is the same regardless the size of the market. The approach to maximize a customer's satisfaction is also the same."
He continued: "As the price of new cars in Malaysia is relatively higher, I assume customers here would expect more. In order to satisfy such high expectations, I believe we need to provide an excellent standard of customer service."
Ueno expressed the importance of making Honda a successful car brand in Malaysia as part of the Japanese automobile manufacturer's strategy to create a strong hold for the brand in Asia. "When I joined Asia-Oceania Division in 1990, it was already widely believed that Asia would be a market with biggest potential. Indeed, from the 90's through to 2000's, Asia's growth and demand of cars grew tremendously. In this fast-growing region, I believe Malaysia plays a core role contributing to Honda's regional growth, due to its advantages of geographic location, human resources and fundamentals for advanced industries. Honda's success in Malaysia will continue to be essential to establish a strong Honda brand in Asia."
Speaking specifically on how he will be able to contribute to the Malaysian operation, Ueno set himself the challenge of maintaining Honda Malaysia's growth momentum. "In Malaysia, Honda has not only established a strong brand image, it has also grown significantly in sales volume by approximately 18,000 units or +2.1% market share in the last three years. By working together with Honda Malaysia's associates and dealers, I hope to upkeep the pace of growth and bring Honda Malaysia's business to its next level," he said.
Ueno started running in US three years ago and have since participated in four marathon races. Through this experience, he has encountered the famous words of Mizuki Noguchi, who is Japan's best and the world's third record holder for women's marathon. "Her line 'The distance you have run never fail to reward you' has become my personal mantra. It means the effort you have made will never be in vain, and that you cannot accomplish anything without continuous efforts," said Ueno.
Similarly for business, there is no success through chance or luck -- continuous effort is the key to accomplishment, which is the ultimate source of joy and challenge.
Honda (ARTA) and Ralph Firman parts company in Sup...


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redpajama_set_name: RedPajamaGithub


text:
Das Wohnhaus Aachener Straße 236 steht in der Stadt Mönchengladbach in Nordrhein-Westfalen im Stadtteil Holt. Es wurde 1926 erbaut. Das Gebäude ist unter Nr. A 025 am 17. Mai 1989 in die Denkmalliste der Stadt Mönchengladbach eingetragen worden.

Lage
Die Aachener Straße führt vom Abteiberg in der Innenstadt von Mönchengladbach nach Südwesten durch die Stadtteile Westend und Holt. Das Haus Nummer 236 liegt ganz im Nordosten des Stadtteils und ca. 20 Meter abseits der Straße nach Nordwesten.

Architektur
Der zweigeschossige Bau hat einen additiv-verkompliziertem Grundriss. Als optisches "Gelenk" der Fassade verbindet ein zylindrischer Baukörper, der die Haustür enthält, den querrechteckigen Haupttrakt mit einem unter einem Winkel von 45° rechts in ihn hineingeführten, gleich hohen in der Fassade gleich langen Baukörper. Dadurch winkelig abgeknickte, aus zwei gleich großen Schenkeln bestehende Fassade enthält im linken Trakt im Erdgeschoss zwei, im ersten Obergeschoss weitere zwei Fenster; der rechte Trakt enthält im Erdgeschoss drei kleine, im ersten Obergeschoss zwei, zur linken Hälfte der Fassade symmetrisch entsprechende Fenster.

Zwischen Erdgeschoss und ersten Obergeschoss befindet sich ein um die Seitenflächen des Gebäudes herumgeführtes Sohlbankgesims. Der turmartig-zylindrische Eingangskörper mit polygonal gebrochener Glockenhaube. An der fünfachsigen Rückseite (Gartenseite) des Hauptbaukörpers mittig eine dreiachsige, aus vier eckig gebrochenen Säulen mit Gebälk bestehende, oben als Balkon für das erste Obergeschoss dienende Loggia. Bis auf die beiden seitlichen Fenster des ersten Obergeschosses wurden alle Fenster der Rückfront zu "Französischen Fenstern" (bis zum Boden herab geführte Fenster) erweitert. In der Rück- und den Seitenflächen des winkelig geführten Walmdaches je eine Fledermausgaube.

Siehe auch
 Liste der Baudenkmäler in Mönchengladbach

Weblinks

Einzelnachweise

Wohngebäude in Mönchengladbach
Baudenkmal in Mönchengladbach
Erbaut in den 1920er Jahren

redpajama_set_name: RedPajamaWikipedia


text:
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Walter Reed National Military Medical Center Achieves LEED Gold, Wins USGBC Honor
BETHESDA, Md. – The Walter Reed National Military Medical Center achieved LEED Gold certification in the LEED for New Construction rating system - an impressive feat for a state-of-the-art medical facility that operates around the clock and uses high-energy medical equipment. On July 20, the project was named Project of the Year - New Construction by the U.S. Green Building Council National Capital Region Chapter.
Under a design-build contract awarded by the Naval Facilities Engineering Command, Clark/Balfour Beatty, A Joint Venture, created the Walter Reed National Military Medical Center on the campus of the National Naval Medical Center in Bethesda, Md. Design efforts were led by HKS Architects, executive architect; Wingler Sharp, architectural renovation and construction design; and Hartman-Cox Architects, historic preservation. Design-build partners integral to achieving LEED certification include Southland Industries (mechanical) and M.C. Dean (electrical).The $826 million project included new construction as well as expansions and renovations to existing hospital facilities. The Walter Reed National Military Medical Center was created through the Base Realignment and Closure Act of 2005.The new construction work that is LEED Gold certified (the renovation work was not submitted for LEED certification) consists of the following projects that were scored together:
America Building (Building A): 560,000-square-foot, six-story outpatient facility providing services for cancer patients, amputees and other outpatient clinical needs. This is the largest outpatient medical building in the military health system.
Arrowhead Building (Building B): 165,000-square-foot, four-story addition to the existing hospital housing emergency in-patient services.
Patient Parking Garage: 939-space, eight-story parking structure located to the east of Building A.
The Navy required LEED Silver for this project, but the design-build team voluntarily went beyond that, earning Gold by achieving 43 points, 30 percent more than required for Silver."Our team is the truest example of how intense collaboration between designers and builders, coupled with an unwavering focus on sustainability throughout all project phrases, can yield exceptional results, even on a demanding healthcare project," said Joe Hogan, Senior Vice President, Clark/Balfour Beatty.Sustainable Design highlights:
The heating, ventilation, and cooling systems incorporate cutting-edge technology such as efficient lighting systems, an Enthalpy heat recovery wheel to transfer energy between exhaust and incoming outside air, and high-efficiency water-cooled centrifugal chillers to save 28 percent energy to heat and cool the building.
The Enthalpy wheel allows the occupants to enjoy 100 percent outside air in lieu of the usually re-circulated air.
The site storm water system performs 25 percent better in preventing flooding downstream than code due to local water pollution issues.
By selecting native and adaptive landscaping, the need for permanent irrigation systems was negated. In addition, water-efficient fixtures reduced water usage by 33 percent inside the building.
Covered parking and new landscaping camouflage cars and reduce the amount of hot pavement that would upset the circadian rhythm of the natural habitat and systems.
Light pollution controls prevent interior and exterior lighting from illuminating the night sky and unnecessarily spilling light onto adjacent plazas and buildings.
All interior materials products are selected based on toxicity, performance and environmentally friendly aspects.
An innovative construction waste management plan diverted 89 percent of waste from landfills.
As we accept the certification for LEED Gold, we recognize our responsibility to the nation, the taxpayers and the American people to be good stewards of the resources that have been provided to us," said Col. Charles Callahan, deputy commander, National Naval Medical Center.New construction and the parking garage submitted for LEED certification was complete in January. Renovations to the existing hospital at National Naval Medical Center and additional work continue through July.
Governor George Deukmejian Courthouse Earns Best Public Project
LONG BEACH, Calif. – Just last week, the Governor George Deukmejian Courthouse project team was honored with the Los Angeles Business Journal's Commercial Real Estate Award for the 2014 Best Public Project.
Governor George Deukmejian Courthouse
Atkinson Earns AGC-WSDOT's Excellence in Contract Administration Award
TACOMA, Wash. – Atkinson Construction was among those honored for administrative and environmental excellence by Associated General Contractors (AGC) of Washington and Washington St
Clark to Construct Pedestrian Access to Walter Reed National Medical Center
BETHESDA, Md. — The Montgomery County Department of Transportation has awarded Clark an $81 million design-build contract for the MD 355 Crossing in Bethesda, Md.
MD 355 Crossing
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I'm going to participate in the "Family Foto" Planting Chums bloghop over at Aspire today.
I took this picture, of the road by our house blanketed in snow, after a mini blizzard in January. Being a Texan at heart (I was born and lived the first nine years of my life there...I still consider myself a Texan!), there's still a little piece of me that gets overjoyed every time it snows. Because we live in Oklahoma, snow is not exactly common, but in a typical winter it snows 3-4 times.
On Christmas Eve, it was snowing and sleeting so badly that we were unable to go to the service that night (the above picture was taken a few weeks later when it snowed again). While I was bummed about not being able to go to church, I loved how everything was shut down and we were able to curl up next to the fireplace with some hot chocolate.
There's something about snow, or any "sort of extreme" (I know a foot is nothing to northerners! :)), that people find slightly humorous.
In the summer of 2006 - while we still live in St. Louis - there was a bad storm that knocked the power out for 5 (very hot) days. Every night there were barbecues as people had to use up the food they had in their freezers and refrigerators before it went bad. Since there was no power, everyone just kind of hung out, as they didn't have the option of sitting in their air conditioned homes. Honestly, I was a bit sad when the power came back on.
My point is, I love snow (I would probably feel differently if I lived in Michigan... :)) and everything associated with it. I love that people help each other out when the weather gets a little worse. And I love the memories I have of it.