Displacement constraints for interactive modeling and animation of articulated structures

This paper presents an integrated set of methods for the automatic construction and interactive animation of solid systems that satisfy specified geometric constraints. Displacement contraints enable the user to design articulated bodies with various degrees of freedom in rotation or in translation at highes and to restrict the scope of the movement at will. The graph of constrained objects may contain closed loops. The animation is achieved by decoupling the free motion of each solid component from the action of the constraints. We do this with iterative tunings in displacements. The method is currently implemented in a dynamically based animation system and takes the physical parameters into account while reestablishing the constraints. In particular, first-order momenta are preserved during this process. The approach would be easy to extend to modeling systems or animation modules without a physical model just by allowing the user to control more parameters.     

The dynamics of articulated rigid bodies for purposes of animation

Curves and surfaces satisfying continuity and smoothness conditions are used in computer graphics to fit spatial data points. In a similar fashion, smooth motions of objects should be available to animators in such a way that the dynamics are correct to the degree required for realism. The motion, like a curve or surface shape, should be controllable by easy manipulations of a set of control parameters or by real-time interaction between the animator and a scene generated by dynamic simulation. In this paper, the objects considered have the form of rigid links joined at hinges to form a tree. This is a reasonable first approximation to human and animal bodies. The equations of motion are formulated with respect to hinge-centered coordinates, and are solved by an efficient technique in time which grows linearly with the number of links.     

Skeleton driven animation based on implicit skinning

Skeleton-driven animation methods have been commonly used in animating 3D characters. In these methods, a skinning process that binds the character surface onto the skeleton is required. This process is usually accomplished manually and is a time-consuming task. In this paper, we propose a novel method for automatically skinning skeletal character models. Given the motion of a skeleton, our method can animate the character model automatically. In our method, each joint coordinate is parameterized by its surrounding local surface. In such a way, the character's surface is implicitly bound onto the skeleton. Character animation is achieved by minimizing an energy function that is carefully designed to prevent unnatural volume changes and to guarantee smooth deformations. Experiments demonstrate the efficiency and excellent performance of our method.     

Scalar-field-guided adaptive shape deformation and animation

In this paper, we propose a novel scalar-field-guided adaptive shape deformation (SFD) technique founded on PDE-based flow constraints and scalar fields of implicit functions. Scalar fields are used as embedding spaces. Upon deformation of the scalar field, a corresponding displacement/velocity field will be generated accordingly, which results in a shape deformation of the embedded object. In our system, the scalar field creation, sketching, and manipulation are both natural and intuitive. The embedded model is further enhanced with self-optimization capability. During the deformation we can also enforce various constraints on embedded models. In addition, this technique can be used to ease the animation design. Our experiments demonstrate that the new SFD technique is powerful, efficient, versatile, and intuitive for shape modeling and animation. This revised version was published online in February 2004 due to typesetting mistakes in the author correction process.     

Self adaptive animation based on user perspective

In this paper we present a new character animation technique in which the animation adapts itself based on the change in the user’s perspective, so that when the user moves and their point of viewing the animation changes, then the character animation adapts itself in response to that change. The resulting animation, generated in real-time, is a blend of key animations provided a priori by the animator. The blending is done with the help of efficient dual-quaternion transformation blending. The user’s point of view is tracked using either computer vision techniques or a simple user-controlled input modality, such as mouse-based input. This tracked point of view is then used to suitably select the blend of animations. We show a way to author and use such animations in both virtual as well as augmented reality scenarios and demonstrate that it significantly heightens the sense of presence for the users when they interact with such self adaptive animations of virtual characters.     

Continuous collision detection for adaptive simulation of articulated bodies

We perform continuous collision detection (CCD) for articulated bodies where motion is governed by an adaptive dynamics simulation. Our algorithm is based on a novel hierarchical set of transforms that represent the kinematics of an articulated body recursively, as described by an assembly tree. The performance of our CCD algorithm significantly improves as the number of active degrees of freedom in the simulation decreases.     

Video-realistic image-based eye animation via statistically driven state machines

In this work we elaborate on a novel image-based system for creating video-realistic eye animations to arbitrary spoken output. These animations are useful to give a face to multimedia applications such as virtual operators in dialog systems. Our eye animation system consists of two parts: eye control unit and rendering engine, which synthesizes eye animations by combining 3D and image-based models. The designed eye control unit is based on eye movement physiology and the statistical analysis of recorded human subjects. As already analyzed in previous publications, eye movements vary while listening and talking. We focus on the latter and are the first to design a new model which fully automatically couples eye blinks and movements with phonetic and prosodic information extracted from spoken language. We extended the already known simple gaze model by refining mutual gaze to better model human eye movements. Furthermore, we improved the eye movement models by considering head tilts, torsion, and eyelid movements. Mainly due to our integrated blink and gaze model and to the control of eye movements based on spoken language, subjective tests indicate that participants are not able to distinguish between real eye motions and our animations, which has not been achieved before.     

Generation of an adaptive simulation driven by product trajectories

The tracking of products trajectories involves major challenges in simulation generation and adaptation. Positioning techniques and technologies have become available and affordable to incorporate more deeply into workshop operations. We present our 2-year effort into developing a general framework in location and manufacturing applications. We demonstrate the features of the proposed applications using a case study, a synthetic flexible manufacturing environment, with product-driven policy, which enables the generation of a location data stream of product trajectories over the whole plant. These location data are mined and processed to reproduce the manufacturing system dynamics in an adaptive simulation scheme. This article proposes an original method for the generation of simulation models in discrete event systems. This method uses the product location data in the running system. The data stream of points (product ID, location, and time) is the starting point for the algorithm to generate a queuing network simulation model.     

Fast animation of lightning using an adaptive mesh

We present a fast method for simulating, animating, and rendering lightning using adaptive grids. The "dielectric breakdown model" is an elegant algorithm for electrical pattern formation that we extend to enable animation of lightning. The simulation can be slow, particularly in 3D, because it involves solving a large Poisson problem. Losasso et al. recently proposed an octree data structure for simulating water and smoke, and we show that this discretization can be applied to the problem of lightning simulation as well. However, implementing the incomplete Cholesky conjugate gradient (ICCG) solver for this problem can be daunting, so we provide an extensive discussion of implementation issues. ICCG solvers can usually be accelerated using "Eisenstat's trick," but the trick cannot be directly applied to the adaptive case. Fortunately, we show that an "almost incomplete Cholesky" factorization can be computed so that Eisenstat's trick can still be used. We then present a fast rendering method based on convolution that is competitive with Monte Carlo ray tracing but orders of magnitude faster, and we also show how to further improve the visual results using jittering     

Design and animation of volume density functions

This paper describes the design and animation of volume density functions for creating images containing gases, solid textures and hypertextures. The development of functions for producing images and animations of gases is explained in detail. Procedures for modelling the geometry of various gases are described. Next, animation techniques for volume density functions are described and examples of the animation of these functions are presented, including steam rising from a teacup, wind effects, marble forming, and gas and liquid flow into an opening in a wall. Solid spaces are introduced as a unifying approach to describing and animating object attributes.     

A locally adaptive peano scanning algorithm

This paper describes an algorithm which builds a ``Peano scanning,' i.e., the reciprocal mapping, from [0, 1]n to [0, 1], of the well-known ``Peano curve.' This Peano scanning is applied to a set of points in [0, 1]n and gives a one-dimensional image of it. Several applications of this technique have already been developed and are presented in this paper.     

Non-linear adaptive robust control of electro-hydraulic systems driven by double-rod actuators

This paper studies the high performance robust motion control of electro-hydraulic servo-systems driven by double-rod hydraulic actuators. The dynamics of hydraulic systems are highly non-linear and the system may be subjected to non-smooth and discontinuous non-linearities due to directional change of valve opening, friction and valve overlap. Aside from the non-linear nature of hydraulic dynamics, hydraulic servosystems also have large extent of model uncertainties. To address these challenging issues, the recently proposed adaptive robust control (ARC) is applied and a discontinuous projection based ARC controller is constructed. The resulting controller is able to take into account the effect of the parameter variations of the inertia load and the cylinder hydraulic parameters as well as the uncertain non-linearities such as the uncompensated friction forces and external disturbances. Non-differentiability of the inherent non-linearities associated with hydraulic dynamics is carefully examined and addressing strategies are provided. Compared with previously proposed ARC controller, the controller in the paper has a more robust parameter adaptation process and may be more suitable for implementation. Finally, the controller guarantees a prescribed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain non-linearities while achieving asymptotic tracking in the presence of parametric uncertainties.     

Uncertainty visualization in medical volume rendering using probabilistic animation

Direct Volume Rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.     

A locally adaptive window for signal matching

This article presents a signal matching algorithm that can select an appropriate window size adaptively so as to obtain both precise and stable estimation of correspondences.Matching two signals by calculating the sum of squared differences (SSD) over a certain window is a basic technique in computer vision. Given the signals and a window, there are two factors that determine the difficulty of obtaining precise matching. The first is the variation of the signal within the window, which must be large enough, relative to noise, that the SSD values exhibit a clear and sharp minimum at the correct disparity. The second factor is the variation of disparity within the window, which must be small enough that signals of corresponding positions are duly compared. These two factors present conflicting requirements to the size of the matching window, since a larger window tends to increase the signal variation, but at the same time tends to include points of different disparity. A window size must be adaptively selected depending on local variations of signal and disparity in order to compute a most-certain estimate of disparity at each point.There has been little work on a systematic method for automatic window-size selection. The major difficulty is that, while the signal variation is measurable from the input, the disparity variation is not, since disparities are what we wish to calculate. We introduce here a statistical model of disparity variation within a window, and employ it to establish a link between the window size and the uncertainty of the computed disparity. This allows us to choose the window size that minimizes uncertainty in the disparity computed at each point. This article presents a theory for the model and the resultant algorithm, together with analytical and experimental results that demonstrate their effectiveness.     

Oxygenation absorption and light scattering driven facial animation of natural virtual human

Multimedia Tools and Applications - The color of skin is one of the key indicators of bodily change that affect facial expressions. The skin colour tenacity is majorly determined by the light...     

Multi-view collaborative locally adaptive clustering with Minkowski metric

Recently, many heterogeneous but related views of data have been generated in a number of applications. Different views may represent distinct aspects of the same data, which often have the same or consensus cluster structure. Discovering cluster structure in multi-view data has become a hot research topic and significant progress has been made in multi-view clustering. However, it remains a challenging issue to exploit the diversity within each view and investigate the relationship across multiple views simultaneously. To address the above issues, in this paper, we extend locally adaptive clustering into a multi-view framework with Minkowski metric and propose a novel approach termed multi-view collaborative locally adaptive clustering with Minkowski metric (MV-CoMLAC). Different from the existing multi-view subspace clustering methods, the proposed approach is capable of simultaneously taking into account the subspace diversity within each view as well as the knowledge across different views. A collaborative strategy is designed to exploit the complementary information from different low-dimensional subspaces. Furthermore, Minkowski metric is utilized to take into account the influence of the L-p distance (p ≥ 0), making our method adaptive to different application tasks. Extensive experiments have been conducted on several multi-view datasets, which demonstrate the superiority of our approach over the existing multi-view clustering methods.     

MTF driven adaptive multiscale bilateral filtering for pansharpening

The recently proposed Bilateral Filter Luminance Proportional (BFLP) method extracts the high-frequency details from panchromatic (Pan) image via a multiscale bilateral filter and adds them proportionally to the multispectral (MS) image. Although this approach seems similar to other multiresolution (MRA) based schemes such as Additive Wavelet proportional Luminance (AWLP) or Generalized Laplacian (GLP) methods, multiscale bilateral filter obtains the detail planes to be injected to MS image by the combination of two Gaussian kernels controlling the transfer of details and performing successively in spatial and range domains, thus it has two parameters to be defined, namely spatial and range parameters. Since the parameter determination step considerably affects the efficiency of the method, in this paper we propose a single parameter bilateral filter by approximating the Gaussian kernel with the bicubic kernel of à trous wavelet transform (ATWT) or modulation transfer function (MTF). Moreover, we adopt an adaptive injection scheme where the range parameter is determined adaptively so as to follow the statistics of the images to be fused. The pansharpening results are compared with ATWT-based methods, as well as some state-of-the-art methods and BFLP. The visual and quantitative comparisons for Système Pour l’Observation de la Terre 7 (SPOT 7) and Pléiades 1A images, field studies supported with UAV (Unmanned Aerial Vehicle) images and digitization results of the chosen areas in Istanbul Technical University (ITU) Maslak campus confirm the superiority of the proposed detail injection approach.     

A global adaptive link position-tracking controller for robot manipulators driven by induction motors

We present an adaptive, partial-state feedback, link position tracking controller for robot manipulators driven by induction motors. The proposed controller compensates for parametric uncertainty in the mechanical subsystem while yielding global asymptotic link position tracking. The proposed controller does not require measurement of rotor flux; furthermore, the controller does not exhibit any singularities. Preliminary experimental and simulation results are provided to illustrate the effectiveness of the proposed controller.     

Stereo matching using gradient similarity and locally adaptive support-weight

Due to the similarities between neighbouring pixels as well as the intensity-value differences between corresponding pixels, classical matching measures based on intensity similarity produce slightly imprecise results. In this study, a gradient similarity-matching measure was implemented in a state-of-the-art local stereo-matching method (an adaptive support-weight algorithm). The new matching measure improved the precision of the results over the classical measures. Using the Middlebury stereo benchmark, when high accuracy was required in the disparity results our algorithm consistently outperformed other adaptive support-weight algorithms using different similarity measures, and it was the best local area-based method compared to the permanent Middlebury table entries.