Synthesizing Two‐character Interactions by Merging Captured Interaction Samples with their Spacetime Relationships

Existing synthesis methods for closely interacting virtual characters relied on user‐specified constraints such as the reaching positions and the distance between body parts. In this paper, we present a novel method for synthesizing new interacting motion by composing two existing interacting motion samples without the need to specify the constraints manually. Our method automatically detects the type of interactions contained in the inputs and determines a suitable timing for the interaction composition by analyzing the spacetime relationships of the input characters. To preserve the features of the inputs in the synthesized interaction, the two inputs will be aligned and normalized according to the relative distance and orientation of the characters from the inputs. With a linear optimization method, the output is the optimal solution to preserve the close interaction of two characters and the local details of individual character behavior. The output animations demonstrated that our method is able to create interactions of new styles that combine the characteristics of the original inputs.     

A Data‐driven Segmentation for the Shoulder Complex

The human shoulder complex is perhaps the most complicated joint in the human body being comprised of a set of three bones, muscles, tendons, and ligaments. Despite this anatomical complexity, computer graphics models for motion capture most often represent this joint as a simple ball and socket. In this paper, we present a method to determine a shoulder skeletal model that, when combined with standard skinning algorithms, generates a more visually pleasing animation that is a closer approximation to the actual skin deformations of the human body. We use a data‐driven approach and collect ground truth skin deformation data with an optical motion capture system with a large number of markers (200 markers on the shoulder complex alone). We cluster these markers during movement sequences and discover that adding one extra joint around the shoulder improves the resulting animation qualitatively and quantitatively yielding a marker set of approximately 70 markers for the complete skeleton. We demonstrate the effectiveness of our skeletal model by comparing it with ground truth data as well as with recorded video. We show its practicality by integrating it with the conventional rendering/animation pipeline.     

Creating and Preserving Vortical Details in SPH Fluid

We present a new method to create and preserve the turbulent details generated around moving objects in SPH fluid. In our approach, a high‐resolution overlapping grid is bounded to each object and translates with the object. The turbulence formation is modeled by resolving the local flow around objects using a hybrid SPH‐FLIP method. Then these vortical details are carried on SPH particles flowing through the local region and preserved in the global field in a synthetic way. Our method provides a physically plausible way to model the turbulent details around both rigid and deformable objects in SPH fluid, and can efficiently produce animations of complex gaseous phenomena with rich visual details.     

Fast and Efficient Skinning of Animated Meshes

Skinning is a simple yet popular deformation technique combining compact storage with efficient hardware accelerated rendering. While skinned meshes (such as virtual characters) are traditionally created by artists, previous work proposes algorithms to construct skinning automatically from a given vertex animation. However, these methods typically perform well only for a certain class of input sequences and often require long pre‐processing times. We present an algorithm based on iterative coordinate descent optimization which handles arbitrary animations and produces more accurate approximations than previous techniques, while using only standard linear skinning without any modifications or extensions. To overcome the computational complexity associated with the iterative optimization, we work in a suitable linear subspace (obtained by quick approximate dimensionality reduction) and take advantage of the typically very sparse vertex weights. As a result, our method requires about one or two orders of magnitude less pre‐processing time than previous methods.     

Exploratory Visualization of Animal Kinematics Using Instantaneous Helical Axes

We present novel visual and interactive techniques for exploratory visualization of animal kinematics using instantaneous helical axes (IHAs). The helical axis has been used in orthopedics, biomechanics, and structural mechanics as a construct for describing rigid body motion. Within biomechanics, recent imaging advances have made possible accurate high‐speed measurements of individual bone positions and orientations during experiments. From this high‐speed data, instantaneous helical axes of motion may be calculated. We address questions of effective interactive, exploratory visualization of this high‐speed 3D motion data. A 3D glyph that encodes all parameters of the IHA in visual form is presented. Interactive controls are used to examine the change in the IHA over time and relate the IHA to anatomical features of interest selected by a user. The techniques developed are applied to a stereoscopic, interactive visualization of the mechanics of pig mastication and assessed by a team of evolutionary biologists who found interactive IHA‐based analysis a useful addition to more traditional motion analysis techniques.     

Fast Particle‐based Visual Simulation of Ice Melting

The visual simulation of natural phenomena has been widely studied. Although several methods have been proposed to simulate melting, the flows of meltwater drops on the surfaces of objects are not taken into account. In this paper, we propose a particle‐based method for the simulation of the melting and freezing of ice objects and the interactions between ice and fluids. To simulate the flow of meltwater on ice and the formation of water droplets, a simple interfacial tension is proposed, which can be easily incorporated into common particle‐based simulation methods such as Smoothed Particle Hydrodynamics. The computations of heat transfer, the phase transition between ice and water, the interactions between ice and fluids, and the separation of ice due to melting are further accelerated by implementing our method using CUDA. We demonstrate our simulation and rendering method for depicting melting ice at interactive frame‐rates.     

Flexible Splicing of Upper‐Body Motion Spaces on Locomotion

This paper presents an efficient technique for synthesizing motions by stitching, or splicing, an upper‐body motion retrieved from a motion space on top of an existing lower‐body locomotion of another motion. Compared to the standard motion splicing problem, motion space splicing imposes new challenges as both the upper and lower body motions might not be known in advance. Our technique is the first motion (space) splicing technique that propagates temporal and spatial properties of the lower‐body locomotion to the newly generated upper‐body motion and vice versa. Whereas existing techniques only adapt the upper‐body motion to fit the lower‐body motion, our technique also adapts the lower‐body locomotion based on the upper body task for a more coherent full‐body motion. In this paper, we will show that our decoupled approach is able to generate high‐fidelity full‐body motion for interactive applications such as games.     

Animated 3D Line Drawings with Temporal Coherence

Producing traditional animation is a laborious task where the key drawings are first drawn by artists and thereafter inbetween drawings are created, whether it is by hand or computer‐assisted. Auto‐inbetweening of these 2D key drawings by computer is a non‐trivial task as 3D depths are missing. An alternate approach is to generate all the drawings by extracting lines directly from animated 3D models frame by frame, concatenating and rendering them together into an animation. However, animation quality generated using this straightforward method bears two problems. Firstly, the animation contains unsatisfactory visual artifacts such as line flickering and popping. This is especially pronounced when the lines are extracted using high‐order derivatives, such as ridges and valleys, from 3D models represented in triangle meshes. Secondly, there is a lack of temporal continuity as each drawing is generated without taking its neighboring drawings into consideration. In this paper, we propose an improved approach over the straightforward method by transferring extracted 3D line drawings of each frame into individual 3D lines and processing them along the time domain. Our objective is to minimize the visual artifacts and incorporate temporal relationship of individual lines throughout the entire animation sequence. This is achieved by creating correspondent trajectory of each line from each frame and applying global optimization on each trajectory. To realize this target, we present a fully automatic novel approach, which consists of (1) a line matching algorithm, (2) an optimizing algorithm, taking into account both the variations of numbers and lengths of 3D lines in each frame, and (3) a robust tracing method for transferring collections of line segments extracted from the 3D models into individual lines. We evaluate our approach on several animated model sequences to demonstrate its effectiveness in producing line drawing animations with temporal coherence.     

Bidirectional Search for Interactive Motion Synthesis

We present an approach to improve the search efficiency for near‐optimal motion synthesis using motion graphs. An optimal or near‐optimal path through a motion graph often leads to the most intuitive result. However, finding such a path can be computationally expensive. Our main contribution is a bidirectional search algorithm. We dynamically divide the search space evenly and merge two search trees to obtain the final solution. This cuts the maximum search depth almost in half and leads to significant speedup. To illustrate the benefits of our approach, we present an interactive sketching interface that allows users to specify complex motions quickly and intuitively.     

Procedural Synthesis using Vortex Particle Method for Fluid Simulation

We propose a fast and effective technique to improve sub‐grid visual details of the grid based fluid simulation. Our method procedurally synthesizes the flow fields coming from the incompressible Navier‐Stokes solver and the vorticity fields generated by vortex particle method for sub‐grid turbulence. We are able to efficiently animate smoke which is highly turbulent and swirling with small scale details. Since this technique does not solve the linear system in high‐resolution grids, it can perform fluid simulation more rapidly. We can easily estimate the influence of turbulent and swirling effect to the fluid flow.     

Wake Synthesis For Shallow Water Equation

In fluid animation, wake is one of the most important phenomena usually seen when an object is moving relative to the flow. However, in current shallow water simulation for interactive applications, this effect is greatly smeared out. In this paper, we present a method to efficiently synthesize these wakes. We adopt a generalized SPH method for shallow water simulation and two way solid fluid coupling. In addition, a 2D discrete vortex method is used to capture the detailed wake motions behind an obstacle, enriching the motion of SWE simulation. Our method is highly efficient since only 2D simulation is required. Moreover, by using a physically inspired procedural approach for particle seeding, DVM particles are only created in the wake region. Therefore, very few particles are required while still generating realistic wake patterns. When coupled with SWE, we show that these patterns can be seen using our method with marginal overhead.     

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.     

The Virtual Director: a Correlation‐Based Online Viewing of Human Motion

Automatic camera control for scenes depicting human motion is an imperative topic in motion capture base animation, computer games, and other animation based fields. This challenging control problem is complex and combines both geometric constraints, visibility requirements, and aesthetic elements. Therefore, existing optimization‐based approaches for human action overview are often too demanding for online computation. In this paper, we introduce an effective automatic camera control which is extremely efficient and allows online performance. Rather than optimizing a complex quality measurement, at each time it selects one active camera from a multitude of cameras that render the dynamic scene. The selection is based on the correlation between each view stream and the human motion in the scene. Two factors allow for rapid selection among tens of candidate views in real‐time, even for complex multi‐character scenes: the efficient rendering of the multitude of view streams, and optimized calculations of the correlations using modified CCA. In addition to the method's simplicity and speed, it exhibits good agreement with both cinematic idioms and previous human motion camera control work. Our evaluations show that the method is able to cope with the challenges put forth by severe occlusions, multiple characters and complex scenes.     

A Key‐Pose Caching System for Rendering an Animated Crowd in Real‐Time

We present a method to accelerate the visualization of large crowds of animated characters. Linear‐blend skinning remains the dominant approach for animating a crowd but its efficiency can be improved by utilizing the temporal and intra‐crowd coherencies that are inherent within a populated scene. Our work adopts a caching system that enables a skinned key‐pose to be re‐used by multi‐pass rendering, between multiple agents and across multiple frames. We investigate two different methods; an intermittent caching scheme (whereby each member of a crowd is animated using only its nearest key‐pose) and an interpolative approach that enables key‐pose blending to be supported. For the latter case, we show that finding the optimal set of key‐poses to store is an NP‐hard problem and present a greedy algorithm suitable for real‐time applications. Both variants deliver a worthwhile performance improvement in comparison to using linear‐blend skinning alone.     

A Single Image Representation Model for Efficient Stereoscopic Image Creation

Computer graphics is one of the most efficient ways to create a stereoscopic image. The process of stereoscopic CG generation is, however, still very inefficient compared to that of monoscopic CG generation. Despite that stereo images are very similar to each other, they are rendered and manipulated independently. Additional requirements for disparity control specific to stereo images lead to even greater inefficiency. This paper proposes a method to reduce the inefficiency accompanied in the creation of a stereoscopic image. The system automatically generates an optimized single image representation of the entire visible area from both cameras. The single image can be easily manipulated with conventional techniques, as it is spatially smooth and maintains the original shapes of scene objects. In addition, a stereo image pair can be easily generated with an arbitrary disparity setting. These convenient and efficient features are achieved by the automatic generation of a stereo camera pair, robust occlusion detection with a pair of Z‐buffers, an optimization method for spatial smoothness, and stereo image pair generation with a non‐linear disparity adjustment. Experiments show that our technique dramatically improves the efficiency of stereoscopic image creation while preserving the quality of the results.     

Interactive,Multiresolution Image‐Space Rendering for Dynamic Area Lighting

Area lights add tremendous realism, but rendering them interactively proves challenging. Integrating visibility is costly, even with current shadowing techniques, and existing methods frequently ignore illumination variations at unoccluded points due to changing radiance over the light's surface. We extend recent image‐space work that reduces costs by gathering illumination in a multiresolution fashion, rendering varying frequencies at corresponding resolutions. To compute visibility, we eschew shadow maps and instead rely on a coarse screen‐space voxelization, which effectively provides a cheap layered depth image for binary visibility queries via ray marching. Our technique requires no precomputation and runs at interactive rates, allowing scenes with large area lights, including dynamic content such as video screens.     

Polynomial Optics: A Construction Kit for Efficient Ray‐Tracing of Lens Systems

Simulation of light transport through lens systems plays an important role in graphics. While basic imaging properties can be conveniently derived from linear models (like ABCD matrices), these approximations fail to describe nonlinear effects and aberrations that arise in real optics. Such effects can be computed by proper ray tracing, for which, however, finding suitable sampling and filtering strategies is often not a trivial task. Inspired by aberration theory, which describes the deviation from the linear ray transfer in terms of wavefront distortions, we propose a ray‐space formulation for nonlinear effects. In particular, we approximate the analytical solution to the ray tracing problem by means of a Taylor expansion in the ray parameters. This representation enables a construction‐kit approach to complex optical systems in the spirit of matrix optics. It is also very simple to evaluate, which allows for efficient execution on CPU and GPU alike, including the computation of mixed derivatives of any order. We evaluate fidelity and performance of our polynomial model, and show applications in high‐quality offline rendering and at interactive frame rates.     

Manifold‐Based 3D Face Caricature Generation with Individualized Facial Feature Extraction

Caricature is an interesting art to express exaggerated views of different persons and things through drawing. The face caricature is popular and widely used for different applications. To do this, we have to properly extract unique/specialized features of a person's face. A person's facial feature not only depends on his/her natural appearance, but also the associated expression style. Therefore, we would like to extract the neutural facial features and personal expression style for different applicaions. In this paper, we represent the 3D neutral face models in BU–3DFE database by sparse signal decomposition in the training phase. With this decomposition, the sparse training data can be used for robust linear subspace modeling of public faces. For an input 3D face model, we fit the model and decompose the 3D model geometry into a neutral face and the expression deformation separately. The neutral geomertry can be further decomposed into public face and individualized facial feature. We exaggerate the facial features and the expressions by estimating the probability on the corresponding manifold. The public face, the exaggerated facial features and the exaggerated expression are combined to synthesize a 3D caricature for a 3D face model. The proposed algorithm is automatic and can effectively extract the individualized facial features from an input 3D face model to create 3D face caricature.     

Easy Generation of Facial Animation Using Motion Graphs

Facial animation is a time‐consuming and cumbersome task that requires years of experience and/or a complex and expensive set‐up. This becomes an issue, especially when animating the multitude of secondary characters required, e.g. in films or video‐games. We address this problem with a novel technique that relies on motion graphs to represent a landmarked database. Separate graphs are created for different facial regions, allowing a reduced memory footprint compared to the original data. The common poses are identified using a Euclidean‐based similarity metric and merged into the same node. This process traditionally requires a manually chosen threshold, however, we simplify it by optimizing for the desired graph compression. Motion synthesis occurs by traversing the graph using Dijkstra's algorithm, and coherent noise is introduced by swapping some path nodes with their neighbours. Expression labels, extracted from the database, provide the control mechanism for animation. We present a way of creating facial animation with reduced input that automatically controls timing and pose detail. Our technique easily fits within video‐game and crowd animation contexts, allowing the characters to be more expressive with less effort. Furthermore, it provides a starting point for content creators aiming to bring more life into their characters.     

A Virtual Director Using Hidden Markov Models

Automatically computing a cinematographic consistent sequence of shots over a set of actions occurring in a 3D world is a complex task which requires not only the computation of appropriate shots (viewpoints) and appropriate transitions between shots (cuts), but the ability to encode and reproduce elements of cinematographic style. Models proposed in the literature, generally based on finite state machine or idiom‐based representations, provide limited functionalities to build sequences of shots. These approaches are not designed in mind to easily learn elements of cinematographic style, nor do they allow to perform significant variations in style over the same sequence of actions. In this paper, we propose a model for automated cinematography that can compute significant variations in terms of cinematographic style, with the ability to control the duration of shots and the possibility to add specific constraints to the desired sequence. The model is parametrized in a way that facilitates the application of learning techniques. By using a Hidden Markov Model representation of the editing process, we demonstrate the possibility of easily reproducing elements of style extracted from real movies. Results comparing our model with state‐of‐the‐art first‐order Markovian representations illustrate these features, and robustness of the learning technique is demonstrated through cross‐validation.