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International audience; Learning based methods have proved effective at high-quality image synthesis tasks, such as content-preserving image rendering with different style, and the generation of new images depicting learned objects. Some of the properties that make neural networks suitable for such tasks, for example robustness to the input's low-level feature, and the ability to retrieve contextual information, are also desirable in 3D shape domain. During last decades, data-driven methods have shown successful results in 3D shape modeling tasks, such as human face and body shape synthesis. Subtle, abstract properties on the geometry that are instantly detected by our eyes but are nontrivial to synthesize, have successfully been achieved by tuning a shape model built from example shapes. Recent successful learning techniques, e.g. deep neural networks, also exploit this shape model, since the regular grid assumption with 2D images does not have a straightforward equivalent in the common shape representation in 3D, thus do not easily generalize to 3D shapes. Here, we concentrate on the 3D facial expression generation task, an important problem in computer graphics and other application domains, where existing data-driven approaches mostly rely on direct shape capture or shape transfer. At the core of our approach is a recurrent neural network with a landmark-based shape representation. The network is trained to estimate a sequence of pose change, thus generate a specific facial expression, by using a set of motion-captured facial expression sequences. Our technique promises to significantly improve the quality of generated expressions while extending the potential applicability of neural networks to sequence of 3D shapes.

International audience; As the symbol of beauty, floral objects have been one of the most popular subjects of artistic drawing. However, designing 3D floral models is generally time-and resource-consuming, because of their structural and geometrical complexity. In this paper, we address the problem of reconstructing floral objects from sketch input. The user draws a relatively clean sketch of a flower and a few additional guide markings from an arbitrary view to rapidly create quality geometric models of flowers. Our system offers a novel modeling scheme compared to several existing flower modelers accepting sketch as input, where the user is required to work with different views, providing step-by-step sketch input. Given the silhouette and the guide strokes, an assumed, common botanical structure is estimated, i.e. a cone for each ring of petals. The cones and the silhouette sketch that we segment into elementary curves are used to retrieve model elements from the pre-constructed shape database. These elements are then placed together around the cone, where an additional, per-element deformation is performed so as to maximize the silhouette similarity between the user sketch and the 3D flower model from the chosen view. Our system has shown to robustly create a variety of flowers in various configurations, including flower models with several petal layers and various blooming degrees, drawn from different views.

International audience; With the recent advances of data acquisition techniques, the compression of various 3D mesh animation data has become an important topic in computer graphics community. In this paper, we present a new spatio-temporal segmentation-based approach for the compression of 3D mesh animations. Given an input mesh sequence , we first compute an initial temporal cut to obtain a small subsequence by detecting the temporal boundary of dynamic behavior. Then, we apply a two-stage vertex clustering on the resulting subsequence to classify the vertices into groups with optimal intra-affinities. After that, we design a temporal segmentation step based on the variations of the principle components within each vertex group prior to performing a PCA-based compression. Our approach can adaptively determine the temporal and spatial segmentation boundaries in order to exploit both temporal and spatial redundancies. We have conducted many experiments on different types of 3D mesh animations with various segmentation configurations. Our comparative studies show the competitive performance of our approach for the compression of 3D mesh animations.

International audience; In this paper, we present a sketch-based modeler that reconstructs a 3D shape by combining a single descriptive sketch and minimal user intervention. The user provides a single 2D drawing in the form of a descriptive sketch, where solid curves describe the visible silhouette, and dashed curves the hidden outline. The curves are partitioned into a set of closed curves in a semi-automatic manner, each of which is consolidated into a closed surface element by solving a constrained optimization problem. The final 3D shape is generated by assembling these surface elements. The algorithmic reconstruction is complemented by allowing users to optionally guide the shape computation or correct any inaccuracy. This is done by successively specifying different kinds of local constraints on sparsely selected points in rotated views, such as adjustment of volume thickness along the projection line, or curvature discontinuity. Consequently, the range and complexity of shapes that can be created from a singleview sketch are significantly extended. We evaluate our solution by reconstructing a wide range of 3D models from sketches of various sources, and visually comparing the reference models and the shapes reconstructed by users.

International audience; Understanding the attentional behavior of the human visual system when visualizing a rendered 3D shape is of great importance for many computer graphics applications. Eye tracking remains the only solution to explore this complex cognitive mechanism. Unfortunately, despite the large number of studies dedicated to images and videos, only a few eye tracking experiments have been conducted using 3D shapes. Thus, potential factors that may influence the human gaze in the specific setting of 3D, are still to be understood. In this work, we conduct two eye-tracking experiments involving 3D shapes, with both static and time-varying camera positions. We propose a method for mapping eye fixations (i.e., where humans gaze) onto the 3D shapes with the aim to produce a benchmark of 3D meshes with fixation density maps, which is publicly available. First, the collected data is used to study the influence of shape, camera position, material and illumination on visual attention. We find that material and lighting have a significant influence on attention, as well as the camera path in the case of dynamic scenes. Then, we compare the performance of four representative state-of-the-art mesh saliency models in predicting ground-truth fixations using two different metrics. We show that, even combined with a center-bias model, the performance of 3D saliency algorithms remains poor at predicting human fixations. To explain their weaknesses, we provide a qualitative analysis of the main factors that attract human attention. We finally provide a quantitative comparison of human-eye fixations and Schelling points and show that their correlation is weak.

The use of cost-efficient and portative devices, such as the Ki-nect, has facilitated the reconstruction of indoor scenes, which has become a field of interest to many. While a large number of applications have been developed for the accurate reconstruction of a scene through efficient registration of multiple scans, the scanning of the scene by the user remains a time-consuming process. This is particularly the case when the scan must not contain any missing data. In this paper, we propose a method requiring only a few shots without any overlapping requirement, which makes the scanning process very simple and light. Given a set point clouds (shots) sparsely taken from an indoor scene, our method seeks for transformations that align the shots together, creating an alignment graph at the same time. It then searches within this graph for the set of transformations corresponding to a possible solution, providing a reconstruction of the scene. Since our method allows missing views and can find all plausible solutions, it can be used to create new scenes by assembling shots obtained from different real or virtual scenes.

We describe a compression method for three-dimensional animation sequences that has notable advantages over existing techniques. We first aggregate the frame data by similarity and reorganize them into clusters, which results in the sequence split into several motion fragments of varying lengths. To minimize the number of clusters and obtain optimal clustering, we perform frame alignment, which eliminates the “global” rigid transformation from each frame data and use only “pose” when evaluating the similarity between frames. We then apply principal component analysis for each cluster, from which we get coordinates of corresponding frames in a reduced dimension. Because similar frames are considered, the number of coefficients required for each frame becomes smaller; thus, we obtain better dimension reduction for a given reconstruction error. Further, we perform intracluster compression based on linear coding. Because every motion fragment presents similar frames, conventional linear predictive coding can be replaced by key frame-based linear coding to achieve minimal reconstruction error. Results show that our method can obtain a high compression ratio, with a limited reconstruction error. Copyright © 2013 John Wiley & Sons, Ltd.

International audience; This paper presents an image-based rendering (IBR) system based on RGB-D images. The input of our system consists of RGB-D images captured at sparse locations in the scene and can be expanded by adding new RGB-D images. The sparsity of RGB-D images increases the usability of our system as the user need not capture a RGB-D image stream in a single shot, which may require careful planning for a hand-held camera. Our system begins with a single RGB-D image and images are incrementally added one by one. For each newly added image, a batch process is performed to align it with previously added images. The process does not include a global alignment step, such as bundle adjustment, and can be completed quickly by computing only local alignments of RGB-D images. Aligned images are represented as a graph, where each node is an input image and an edge contains relative pose information between nodes. A novel view image is rendered by picking the nearest input as the reference image and then blending the neighboring images based on depth information in real time. Experimental results with indoor scenes using Microsoft Kinect demonstrate that our system can synthesize high quality novel view images from a sparse set of RGB-D images.

This paper presents a new technique which makes use of deformation and motion properties between animated meshes for finding their spatial correspondences. Given a pair of animated meshes exhibiting a semantically similar motion, we compute a sparse set of feature points on each mesh and compute spatial correspondences among them so that points with similar motion behavior are put in correspondence. At the core of our technique is our new, dynamic feature descriptor named AnimHOG, which encodes local deformation characteristics. AnimHOG is ob-tained by computing the gradient of a scalar field inside the spatiotemporal neighborhood of a point of interest, where the scalar values are obtained from the deformation characteristic associated with each vertex and at each frame. The final matching has been formulated as a discreet optimization problem that finds the matching of each feature point on the source mesh so that the descriptor similarity between the corresponding feature pairs as well as compatibility and consistency as measured across the pairs of correspondences are maximized. Consequently, reliable correspondences can be found even among the meshes of very different shape, as long as their motions are similar. We demonstrate the performance of our technique by showing the good quality of matching results we obtained on a number of animated mesh pairs.

We introduce in this paper a new method for smooth foldover-free warping of images. It allows users to specify the constraints in two different ways: positional constraints to constrain the position of points in the image and gradient constraints to constrain the orientation and scaling of some parts of the image. We then show how our method is used for texture mapping with hard constraints. We start with an unconstrained planar embedding of the target mesh calculated with conventional methods. In order to obtain a mapping that satisfies the user-defined constraints, we use our warping method to align the features of the texture image with those of the unconstrained embedding. Compared to previous work, our method generates a smoother texture mapping and offers higher level of control for defining the constraints.

**************** **************** **************** **************** **************** ************* The advent of 3D scanning technology has allowed effective measurement and analysis of breast size and shape, attracting interests by plastic surgeons, brassier designers, etc. Much work remains, however, before 3D scanning systems can be successfully used in automated analysis and synthesis of the breast—filtering noise, filling holes, and, in case a statistical analysis is desired, finding correspondence among each scan data. Moreover, analysis of a sagged breast is difficult to obtain, due to occlusions. In this paper, we address the problems and specific issues of using 3D scan data for the analysis and synthesis of breast models. The goal of our work is to build a breast modeler which can help both surgeons and garment designers in analyzing breast volume and surface measurements. Given enough samples of scanned breasts, our modeler can generate highly realistic breast shape, with some expected and consistent variability. Copyright # 2007 John Wiley & Sons, Ltd.

In this chapter, we are concerned with the problem of modeling personalized body models from one or more 2D photos. One of the key tasks in this setting is the 3D shape recovery from the image, a yet-to-be-done task in computer vision which has traditionally been done using just geometric techniques. With our target objects limited to the human body, we try and make the problem easier and the solution more robust and efficient, by making use of high-quality shape data that has previously been acquired from 3D scanners. Based on a compact shape space, which has been built from a collection of range scans of real human body, we formulate the problem as an optimization one and search for the shape parameters that best matches the input silhouette. Texture coordinates are then generated by projecting the resulting shape onto the front and back images. In the presence of noise or missing views, our technique has a bias toward representing, as much as possible, the previously acquired collective knowledge on the body shape. As a result, efficiency is gained due to the fact that a model is generated by interpolating quality shapes from the body scans.

In this article, we describe the need for telemedicine standards and classify various standards of telemedicine. The emerging issues in telecommunications standards, the application of these standards in the health care industry, and future development directions of telemedicine standards are described. Especially, the telemedicine code standardization of drugs and health care providers, multimedia-conferencing-based ISO telemedicine standards, information security management, wireless technology advances for telemedicine using sensor networks, and future standardization issues and considerations are introduced and discussed in detail.

We discuss a set of techniques based on examples for generating realistic, controllable human whole-body models. Users are assisted in automatically generating a new model or modifying an existing one by controlling the parameters provided. Our approach is based on examples and consists of three major parts. First, each example from the 3D range scanner is preprocessed so that the topology of all examples is identical. Second, the system that we call the modeling synthesizer learns from these examples the correlation between the parameters and the body geometry. After this learning process the synthesizer is devoted to the generation of appropriate shape and proportion of the body geometry through interpolation. Finally, we demonstrate our modifier synthesizer for more subtle manipulations of example models, using high-level parameters such as fat percentage. On any synthesized model, the underlying bone and skin structure is properly adjusted, so that the model remains completely animatable using the joint animation. By allowing automatic modification from a set of parameters, our approach may eventually lead to the automatic generation of a variety of population models.

The Internet is a compelling channel for selling garments. Several recent initiatives by companies such as Nordstrom, Macy's, and Lands End focus on made-to-measure manufacturing and shopping via the Internet. We present a Web application that provides more powerful access to and manipulation of clothing to facilitate clothing design, pattern derivation, and sizing. We apply 3D graphics technology to help create and simulate the virtual store.

This paper presents a system to create mirror-symmetric surfaces from free-form sketches. The system takes as input a hand-drawn sketch and generates a surface whose silhouette approximately matches the input sketch. The input sketch typically consists of a set of curves connected at their endpoints, forming T-junctions and cusps. Our system is able to identify the skewed-mirror and translational symmetry between the hand-drawn curves and uses this information to reconstruct the occluded parts of the surface and its 3D shape.

In this work, we record a dancer's rhythmic movement with background music. The captured motion sequences are then segmented into dozens of motion clips, to construct a motion database consisting of sets of labeled motion clips. Many of these motion clips contain short and rapid transition from one main dancing motion to another, which causes unnatural, awkward movements when they are connected in different orders than the original sequence. In this paper, we describe our approach for automatically detecting the transition parts in the segmented motion clips. For each motion clip, we model the motion data using the Gaussian mixture model (GMM) and use the resulting distribution cluster map to improve the efficiency and convergence of the clustering, principal component analysis (PCA) has been applied to the motion data prior to performing GMM. Experiments and comparative analysis show that this PCA-based GMM method effectively performs transition detection on the segmented motion clips.

Tight-fit cloth pressure provides important clue on how well a cloth fits to a body and thus on how comfortable the wearer feels with the cloth. Traditionally-used pressure sensor devices are expensive, sensitive to the experimental environment, and difficult to reproduce. In this paper, a physically-based cloth simulator has been tested for its usability as to measuring the cloth pressure, in order to replace physical measurement of cloth pressure that requires careful operation of pressure sensors. We use existing cloth simulator based on a particle system and measure spring forces exerted on each particle along its normal direction, divided by the summed area of triangles adjacent to that particle. To quantitatively validate the pressure values from the simulator, we have conducted comparative analysis on a set of thin-shell cylindrical tubes --- clothing pressure values have been measured by theoretical estimation and physical experiments using pressure sensors, and compared with those measured by the simulation. While their absolute pressure values differ from each other they exhibit a consistent tendency. From these comparative studies we concluded that cloth simulator can actually be used to measure tight-fit cloth pressure, and further conducted the clothing pressure measure on 3D human body models using the simulator.

When 3D objects occlude each other or self-occlude, their drawings typically consist of a set of contours that might partially overlap or self-overlap. The authors' method infers the hidden parts of contours and creates a smooth 3D shape matching those contours by solving a set of optimization problems

We present our framework that efficiently synchronizes dancing motion of a virtual character according to music input. Two modules have been developed to achieve this. A music server analyzes audio input on the fly and extracts information on music such as tempo, time signature, and beat time. A motion client chooses and displays motion clips according to the music information, by time-warping and synchronizing the start time of motion clips with the music beat time. As a preprocessing step, we construct a database of motion clips using captured dancing motion of a dancer. A PCA-based method is presented to easily identify transiting parts from each motion clip.

We present a data-driven shape model for reconstructing human body models from one or more 2D photos. One of the key tasks in reconstructing the 3D model from image data is shape recovery, a task done until now in utterly geometric way, in the domain of human body modeling. In contrast, we adopt a data-driven, parameterized deformable model that is acquired from a collection of range scans of real human body. The key idea is to complement the image-based reconstruction method by leveraging the quality shape and statistic information accumulated from multiple shapes of range-scanned people. In the presence of ambiguity either from the noise or missing views, our technique has a bias towards representing as much as possible the previously acquired ‘knowledge' on the shape geometry. Texture coordinates are then generated by projecting the modified deformable model onto the front and back images. Our technique has shown to reconstruct successfully human body models from minimum number images, even from a single image input.

We present two complementary methodologies to automatically generate 3D surfaces of human bodies including internal bones. The first methodology is a data-driven approach and uses a database of human bodies in conjunction with a learning model synthesizer to create 3D surfaces subject to desired shape and size. In the second methodology, we use medical scanning devices to capture the internal anatomy of the individual and reconstruct 3D surfaces of bones. The final result is an animatable human body including skin external surface as well as the internal skeleton.

Although graphical human modeling has been a long sought subject in computer graphics, when it comes to dealing with real-time applications, it raises a number of unique requirements for both CG artists and researchers. For example, for a real-time performance it is highly desirable to lighten the geometry and optimize the runtime animation of characters wherever possible. An important need in such systems is to assist the CG artists in seamlessly transforming their modeling work from CG packages to the real-time application, without limiting their expressivity. In this paper we describe techniques by which dressed human characters are modeled, loaded into VR scenes, and simulated in real-time. In doing so, we point out challenging issues and solutions that such tasks imply at each phase of modeling and simulation. In particular, we turn our attention to what we consider as key technical components: body and clothes. Case studies show that our modelers successfully meet the need of real-time requirements over a wide variety of scenarios.

Summary form only given. Appropriate combinations of statistical and geometric methods permit modeling and estimation of complex objects. This paper investigates such combinations in the modeling of human body models in computer graphics. By concentrating on such a particular class of objects, we show how example based techniques together with interpolation ones can exploit existing data and knowledge about body shape and size modelling. Graphical modeling of human bodies obviously has applications in the entertainment industry for character animation and simulations involving people. Other applications range from virtual reality to ergonomic and manufacturing studies.

Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. We study the problem and solutions to automatic modeling of animatable virtual humans. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.

Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. The problem and solutions to automatic modeling of animatable virtual humans are studied. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.

Simulating virtual environments populated with virtual but realistic crowds requires dozens of different face and body geometries. We present GENESIS (GENeration of E-population based on Statistical Information), an application that allows automatic generation of desired population models. The aim of this application is to generate any population group that is statistically calculated to satisfy given properties. We divide the population generation module into the face part and the body part. Each of them is based on a database, which is an organization of information collected and processed from the real population dataset. Upon the specification of the population parameters by the user, GENESIS transforms them onto a number Of queries to the underlying databases, and process the results from the database in a statistical manner, allowing dynamic generation of desired number of individuals while satisfying the given properties of on the overall population.

Presents the VPARK (Virtual Park) system, which includes a networked virtual environment (NVE) system called W-VLNET and an "attraction building system" that is able to create and modify the attractions used in the NVE. Both systems have been developed in the Windows NT environment. The paper outlines the techniques for communication, scene management, facial and body animation, and general user interaction modules. The use of VRML97 and MPEG-4 SHNC is overviewed for the purpose of outlining the compatability of the system with other similar virtual reality systems. The software provides realistic virtual actors as well as sets of high-level actions that are applicable to them in real-time. Related issues on obtaining actor models and animating them in real time are presented. The creation process of an attraction incorporates assembling animation units through a timeline. Using this software, the users are able to bring their own scenario-based applications into a shared virtual environment.

Presents our work on a level-of-detail (LoD) technique for human-like face models in virtual environments. Conventional LoD techniques have been adapted to allow facial animation on simplified geometric models. This includes the optimization of both geometric and animation parameters. Simplified models are generated in a region-based manner, considering the mobility of each region. The animation process is decomposed into two sub-processes, and each step is optimized. In the MPA (minimum perceptible action) level optimization, a hierarchical structure is devised for the multi-level animation model. The deformation level is simplified by reducing the number of control points. At run-time, the animation level is selected in combination with viewpoint information at the geometric level.

This paper describes a complete methodology for cloning and dressing people by using a website. The input is simple photographs or body measurements that anybody can produce in any environment. Then the Web-based virtual-try-on allows users to see them dressed. The basic methodology uses a pre-calculated generic database to produce personally sized bodies and animate garments on a Web application.

Mobile Geographic Information Systems are a versatile extension of traditional GIS and can provide GIS data between the field and the office. This paper addresses the challenges imposed on telecommunications infrastructure management by disaster events. MGIS are presented as tools that provide meaningful real-time communication between field workers and management.

The public are becoming increasingly concerned about the privacy invasion via the internet. The information privacy in telecommunications service management, privacy laws, especially the Online Privacy Act of 2003, and use of Platform for Privacy Preference (P3P) are discussed in this paper. Then, a quantification model using P3P in privacy control is suggested.