Real‐Time Multi‐View Facial Capture with Synthetic Training

We present a real‐time multi‐view facial capture system facilitated by synthetic training imagery. Our method is able to achieve high‐quality markerless facial performance capture in real‐time from multi‐view helmet camera data, employing an actor specific regressor. The regressor training is tailored to specified actor appearance and we further condition it for the expected illumination conditions and the physical capture rig by generating the training data synthetically. In order to leverage the information present in live imagery, which is typically provided by multiple cameras, we propose a novel multi‐view regression algorithm that uses multi‐dimensional random ferns. We show that higher quality can be achieved by regressing on multiple video streams than previous approaches that were designed to operate on only a single view. Furthermore, we evaluate possible camera placements and propose a novel camera configuration that allows to mount cameras outside the field of view of the actor, which is very beneficial as the cameras are then less of a distraction for the actor and allow for an unobstructed line of sight to the director and other actors. Our new real‐time facial capture approach has immediate application in on‐set virtual production, in particular with the ever‐growing demand for motion‐captured facial animation in visual effects and video games.     

Data‐guided Model Predictive Control Based on Smoothed Contact Dynamics

In this paper, we propose an efficient data‐guided method based on Model Predictive Control (MPC) to synthesize a full‐body motion. Guided by a reference motion, our method repeatedly plans the full‐body motion to produce an optimal control policy for predictive control while sliding the fixed‐span window along the time axis. Based on this policy, the method computes the joint torques of a character at every time step. Together with contact forces and external perturbations if there are any, the joint torques are used to update the state of the character. Without including the contact forces in the control vector, our formulation of the trajectory optimization problem enables automatic adjustment of contact timings and positions for balancing in response to environmental changes and external perturbations. For efficiency, we adopt derivative‐based trajectory optimization on top of state‐of‐the‐art smoothed contact dynamics. Use of derivatives enables our method to run much faster than the existing sampling‐based methods. In order to further accelerate the performance of MPC, we propose efficient numerical differentiation of the system dynamics of a full‐body character based on two schemes: data reuse and data interpolation. The former scheme exploits data dependency to reuse physical quantities of the system dynamics at near‐by time points. The latter scheme allows the use of derivatives at sparse sample points to interpolate those at other time points in the window. We further accelerate evaluation of the system dynamics by exploiting the sparsity of physical quantities such as Jacobian matrix resulting from the tree‐like structure of the articulated body. Through experiments, we show that the proposed method efficiently can synthesize realistic motions such as locomotion, dancing, gymnastic motions, and martial arts at interactive rates using moderate computing resources.     

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.     

ProcDef: Local‐to‐global Deformation for Skeleton‐free Character Animation

Animations of characters with flexible bodies such as jellyfish, snails, and, hearts are difficult to design using traditional skeleton‐based approaches. A standard approach is keyframing, but adjusting the shape of the flexible body for each key frame is tedious. In addition, the character cannot dynamically adjust its motion to respond to the environment or user input. This paper introduces a new procedural deformation framework (ProcDef) for designing and driving animations of such flexible objects. Our approach is to synthesize global motions procedurally by integrating local deformations. ProcDef provides an efficient design scheme for local deformation patterns; the user can control the orientation and magnitude of local deformations as well as the propagation of deformation signals by specifying line charts and volumetric fields. We also present a fast and robust deformation algorithm based on shape‐matching dynamics and show some example animations to illustrate the feasibility of our framework.     

Reconstructing Animated Meshes from Time‐Varying Point Clouds

In this paper, we describe a novel approach for the reconstruction of animated meshes from a series of time‐deforming point clouds. Given a set of unordered point clouds that have been captured by a fast 3‐D scanner, our algorithm is able to compute coherent meshes which approximate the input data at arbitrary time instances. Our method is based on the computation of an implicit function in ?⁴ that approximates the time‐space surface of the time‐varying point cloud. We then use the four‐dimensional implicit function to reconstruct a polygonal model for the first time‐step. By sliding this template mesh along the time‐space surface in an as‐rigid‐as‐possible manner, we obtain reconstructions for further time‐steps which have the same connectivity as the previously extracted mesh while recovering rigid motion exactly. The resulting animated meshes allow accurate motion tracking of arbitrary points and are well suited for animation compression. We demonstrate the qualities of the proposed method by applying it to several data sets acquired by real‐time 3‐D scanners.     

Visualization of Particle‐based Data with Transparency and Ambient Occlusion

Particle‐based simulation techniques, like the discrete element method or molecular dynamics, are widely used in many research fields. In real‐time explorative visualization it is common to render the resulting data using opaque spherical glyphs with local lighting only. Due to massive overlaps, however, inner structures of the data are often occluded rendering visual analysis impossible. Furthermore, local lighting is not sufficient as several important features like complex shapes, holes, rifts or filaments cannot be perceived well. To address both problems we present a new technique that jointly supports transparency and ambient occlusion in a consistent illumination model. Our approach is based on the emission‐absorption model of volume rendering. We provide analytic solutions to the volume rendering integral for several density distributions within a spherical glyph. Compared to constant transparency our approach preserves the three‐dimensional impression of the glyphs much better. We approximate ambient illumination with a fast hierarchical voxel cone‐tracing approach, which builds on a new real‐time voxelization of the particle data. Our implementation achieves interactive frame rates for millions of static or dynamic particles without any preprocessing. We illustrate the merits of our method on real‐world data sets gaining several new insights.     

Data‐Driven Reconstruction of Human Locomotion Using a Single Smartphone

Generating a visually appealing human motion sequence using low‐dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system.     

Motion Retrieval Using Low‐Rank Subspace Decomposition of Motion Volume

This paper proposes a novel framework that allows for a flexible and an efficient retrieval of motion capture data in huge databases. The method first converts an action sequence into a novel representation, i.e. the Self‐Similarity Matrix (SSM), which is based on the notion of self‐similarity. This conversion of the motion sequences into compact and low‐rank subspace representations greatly reduces the spatiotemporal dimensionality of the sequences. The SSMs are then used to construct order‐3 tensors, and we propose a low‐rank decomposition scheme that allows for converting the motion sequence volumes into compact lower dimensional representations, without losing the nonlinear dynamics of the motion manifold. Thus, unlike existing linear dimensionality reduction methods that distort the motion manifold and lose very critical and discriminative components, the proposed method performs well even when inter‐class differences are small or intra‐class differences are large. In addition, the method allows for an efficient retrieval and does not require the time‐alignment of the motion sequences. We evaluate the performance of our retrieval framework on the CMU mocap dataset under two experimental settings, both demonstrating promising retrieval rates.     

A Data‐Driven Framework for Visual Crowd Analysis

We present a novel approach for analyzing the quality of multi‐agent crowd simulation algorithms. Our approach is data‐driven, taking as input a set of user‐defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state‐of‐the‐art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per‐agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real‐time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely‐used methods, including a simulation from a commercial game.     

An Exploratory Technique for Coherent Visualization of Time‐varying Volume Data

The selection of an appropriate global transfer function is essential for visualizing time‐varying simulation data. This is especially challenging when the global data range is not known in advance, as is often the case in remote and in‐situ visualization settings. Since the data range may vary dramatically as the simulation progresses, volume rendering using local transfer functions may not be coherent for all time steps. We present an exploratory technique that enables coherent classification of time‐varying volume data. Unlike previous approaches, which require pre‐processing of all time steps, our approach lets the user explore the transfer function space without accessing the original 3D data. This is useful for interactive visualization, and absolutely essential for in‐situ visualization, where the entire simulation data range is not known in advance. Our approach generates a compact representation of each time step at rendering time in the form of ray attenuation functions, which are used for subsequent operations on the opacity and color mappings. The presented approach offers interactive exploration of time‐varying simulation data that alleviates the cost associated with reloading and caching large data sets.     

Semi‐Stochastic Tilings for Example‐Based Texture Synthesis

We investigate semi‐stochastic tilings based on Wang or corner tiles for the real‐time synthesis of example‐based textures. In particular, we propose two new tiling approaches: (1) to replace stochastic tilings with pseudo‐random tilings based on the Halton low‐discrepancy sequence, and (2) to allow the controllable generation of tilings based on a user‐provided probability distribution. Our first method prevents local repetition of texture content as common with stochastic approaches and yields better results with smaller sets of utilized tiles. Our second method allows to directly influence the synthesis result which—in combination with an enhanced tile construction method that merges multiple source textures—extends synthesis tasks to globally‐varying textures. We show that both methods can be implemented very efficiently in connection with tile‐based texture mapping and also present a general rule that allows to significantly reduce resulting tile sets.     

Crowd sculpting: A space‐time sculpting method for populating virtual environments

We introduce “Crowd Sculpting”: a method to interactively design populated environments by using intuitive deformation gestures to drive both the spatial coverage and the temporal sequencing of a crowd motion. Our approach assembles large environments from sets of spatial elements which contain inter‐connectible, periodic crowd animations. Such a “Crowd Patches” approach allows us to avoid expensive and difficult‐to‐control simulations. It also overcomes the limitations of motion editing, that would result into animations delimited in space and time. Our novel methods allows the user to control the crowd patches layout in ways inspired by elastic shape sculpting: the user creates and tunes the desired populated environment through stretching, bending, cutting and merging gestures, applied either in space or time. Our examples demonstrate that our method allows the space‐time editing of very large populations and results into endless animation, while offering real‐time, intuitive control and maintaining animation quality.     

Visual Analysis of Trajectories in Multi‐Dimensional State Spaces

Multi‐dimensional data originate from many different sources and are relevant for many applications. One specific sub‐type of such data is continuous trajectory data in multi‐dimensional state spaces of complex systems. We adapt the concept of spatially continuous scatterplots and spatially continuous parallel coordinate plots to such trajectory data, leading to continuous‐time scatterplots and continuous‐time parallel coordinates. Together with a temporal heat map representation, we design coordinated views for visual analysis and interactive exploration. We demonstrate the usefulness of our visualization approach for three case studies that cover examples of complex dynamic systems: cyber‐physical systems consisting of heterogeneous sensors and actuators networks (the collection of time‐dependent sensor network data of an exemplary smart home environment), the dynamics of robot arm movement and motion characteristics of humanoids.     

Learnt Real‐time Meshless Simulation

We present a new real‐time approach to simulate deformable objects using a learnt statistical model to achieve a high degree of realism. Our approach improves upon state‐of‐the‐art interactive shape‐matching meshless simulation methods by not only capturing important nuances of an object's kinematics but also of its dynamic texture variation. We are able to achieve this in an automated pipeline from data capture to simulation. Our system allows for the capture of idiosyncratic characteristics of an object's dynamics which for many simulations (e.g. facial animation) is essential. We allow for the plausible simulation of mechanically complex objects without knowledge of their inner workings. The main idea of our approach is to use a flexible statistical model to achieve a geometrically‐driven simulation that allows for arbitrarily complex yet easily learned deformations while at the same time preserving the desirable properties (stability, speed and memory efficiency) of current shape‐matching simulation systems. The principal advantage of our approach is the ease with which a pseudo‐mechanical model can be learned from 3D scanner data to yield realistic animation. We present examples of non‐trivial biomechanical objects simulated on a desktop machine in real‐time, demonstrating superior realism over current geometrically motivated simulation techniques.     

Topology Aware Data‐Driven Inverse Kinematics

Creating realistic human movement is a time consuming and labour intensive task. The major difficulty is that the user has to edit individual joints while maintaining an overall realistic and collision free posture. Previous research suggests the use of data‐driven inverse kinematics, such that one can focus on the control of a few joints, while the system automatically composes a natural posture. However, as a common problem of kinematics synthesis, penetration of body parts is difficult to avoid in complex movements. In this paper, we propose a new data‐driven inverse kinematics framework that conserves the topology of the synthesizing postures. Our system monitors and regulates the topology changes using the Gauss Linking Integral (GUI), such that penetration can be efficiently prevented. As a result, complex motions with tight body movements, as well as those involving interaction with external objects, can be simulated with minimal manual intervention. Experimental results show that using our system, the user can create high quality human motion in real‐time by controlling a few joints using a mouse or a multi‐touch screen. The movement generated is both realistic and penetration free. Our system is best applied for interactive motion design in computer animations and games.     

Dynamic Multi‐View Exploration of Shape Spaces

Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point. We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual‐space nature of these spaces. The high‐dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space. A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population‐object duality with a new type of view aimed at shape comparison. This new “shape evolution view” visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above. Our three‐view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU‐accelerated implementation, and show the effectiveness of the three‐view approach using a number of real‐world cases. In these, we demonstrate how this multi‐view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error.     

Editing and Synthesizing Two‐Character Motions using a Coupled Inverted Pendulum Model

This study aims to develop a controller for use in the online simulation of two interacting characters. This controller is capable of generalizing two sets of interaction motions of the two characters based on the relationships between the characters. The controller can exhibit similar motions to a captured human motion while reacting in a natural way to the opponent character in real time. To achieve this, we propose a new type of physical model called a coupled inverted pendulum on carts that comprises two inverted pendulum on a cart models, one for each individual, which are coupled by a relationship model. The proposed framework is divided into two steps: motion analysis and motion synthesis. Motion analysis is an offline preprocessing step, which optimizes the control parameters to move the proposed model along a motion capture trajectory of two interacting humans. The optimization procedure generates a coupled pendulum trajectory which represents the relationship between two characters for each frame, and is used as a reference in the synthesis step. In the motion synthesis step, a new coupled pendulum trajectory is planned reflecting the effects of the physical interaction, and the captured reference motions are edited based on the planned trajectory produced by the coupled pendulum trajectory generator. To validate the proposed framework, we used a motion capture data set showing two people performing kickboxing. The proposed controller is able to generalize the behaviors of two humans to different situations such as different speeds and turning speeds in a realistic way in real time.     

ManyLoDs: Parallel Many‐View Level‐of‐Detail Selection for Real‐Time Global Illumination

Level‐of‐Detail structures are a key component for scalable rendering. Built from raw 3D data, these structures are often defined as Bounding Volume Hierarchies, providing coarse‐to‐fine adaptive approximations that are well‐adapted for many‐view rasterization. Here, the total number of pixels in each view is usually low, while the cost of choosing the appropriate LoD for each view is high. This task represents a challenge for existing GPU algorithms. We propose ManyLoDs, a new GPU algorithm to efficiently compute many LoDs from a Bounding Volume Hierarchy in parallel by balancing the workload within and among LoDs. Our approach is not specific to a particular rendering technique, can be used on lazy representations such as polygon soups, and can handle dynamic scenes. We apply our method to various many‐view rasterization applications, including Instant Radiosity, Point‐Based Global Illumination, and reflection/refraction mapping. For each of these, we achieve real‐time performance in complex scenes at high resolutions.