MIG '17: Proceedings of the Tenth International Conference on Motion in Games

Crowd-aware environment design is a complex combinatorial decision process, where small changes in a design may affect crowd flow patterns in unexpected and potentially unintuitive ways. Existing solutions rely on expert intuition, best practices, or automation. To address the dimensionality and complexity of the design process, we propose leveraging automation and human creativity at a large scale akin to crowd sourcing, within a gamified collaborative design framework. Using our system, "players" (novice users or experts) can rapidly iterate on their designs while soliciting feedback from computer simulations of crowd movement and the designs of other players. Our approach affords a new way of thinking of the solution space in that it inherently supports competitive collaboration, co-design, and crowd sourced solutions. We evaluate our framework through a preliminary user study.

Crowd simulations facilitate the study of how an environment layout impacts the movement and behavior of its inhabitants. However, simulations are computationally expensive, which make them infeasible when used as part of interactive systems (e.g., Computer-Assisted Design software). Machine learning models, such as neural networks (NN), can learn observed behaviors from examples, and can potentially offer a rational prediction of a crowd's behavior efficiently. To this end, we propose a method to predict the aggregate characteristics of crowd dynamics using regression neural networks (NN). We parametrize the environment, the crowd distribution and the steering method to serve as inputs to the NN models, while a number of common performance measures serve as the output. Our preliminary experiments show that our approach can help users evaluate a large number of environments efficiently.

Virtual surgery is a serious game which provides an opportunity to acquire cognitive and technical surgical skills via virtual surgical training and planning. However, interactively and realistically manipulating the human organ and simulating its motion under interaction is still a challenging task in this field. The underlying reason for this issue is the conflict requirements for physical constraints with high fidelity and real-time performance. To achieve realistic simulation of human organ motion with volume conservation, smooth interpolation under large deformation and precise frictional contact mechanics of global behavior in surgical scenario. This paper presents a novel and effective patch Green coordinates based interpolation for embedded deformable model to achieve the volume-preserving and smooth interpolation effects. Besides, we resolve the frictional contact mechanics for embedded deformable model, and further provide the precise boundary conditions for mechanical solver. In addition, our embedded deformable model is based on the total lagrangian explicit dynamics (TLED) finite element method (FEM) solver, which can well handle the large biological tissue deformation with both nonlinear geometric and material properties. In real compression experiments, our method can achieve liver deformation with average accuracy of 3.02 mm. Besides, the experimental results demonstrate that our method can also achieve smoother interpolation and volume-preserving effects than original embedded deformable model, and allows complex and accurate organ motion with mechanical interactions in virtual surgery.

We introduce a new collision proxy for example-based deformable bodies. Specifically, we approximate the deforming geometry as a union of spheres. During pre-computation we perform a sphere packing on the input, undeformed geometry. Then, for each example pose, we move and resize the spheres to approximate the example. During runtime we blend together these positions and radii, using the same skinning weights we use for the geometry. We demonstrate the method on a car crash example, where we achieve an overall speedup of 5--20 times, depending on the resolution of the collision proxy geometry.

In this paper, we revisit the problem online reclustering in clustered shape matching simulations and propose an approach that employs two nonlinear optimizations to create new clusters. The first optimization finds the embedding of particles and clusters into three-dimensional space that minimizes elastic energy. The second finds the optimal location for the new cluster, working in this embedded space. The result is an approach that is more robust in the presence of elastic deformation. We also experimentally verify that our clustered shape matching approach converges as the number of clusters increases, suggesting that our reclustering approach does not change the underlying material properties. Further, we demonstrate that particle resampling is not strictly necessary in our framework allowing us to trivially conserve volume. Finally, we highlight an error in estimating rotations in the original shape-matching work [Müller et al. 2005] that has been repeated in much of the follow up work.

We present an immersive Virtural Reality (VR) experience developed through a unique combination of technologies including an actuated hardware rig; a physics model with a responsive control routine; and an interactive 3D gamelike experience. Specifically, this paper introduces a physics-based communication framework that allows force-driven interaction to be conveyed to a user through a physics-based proxy. Because the framework is generic and extendable, the application supports a variety of interaction modes, constrained by the limitations of the physical full-body haptic rig. To showcase the technology, we highlight the experience of riding locomoting robots and vehicles placed in an immersive VR setting.

Many approaches for motion processing or motion analysis employ Dynamic Time Warping (DTW) for temporally aligning an input movement with a reference movement. DTW, however, does not work online since it requires the complete input trajectory. Its online extension Open-End DTW can lead to poor alignments. In this paper we propose Weight-Optimized Open-End DTW, which combines path-length weighting and joint weights optimized from training data. We demonstrate our method to work online and to outperform Open-End DTW in terms of alignment quality.

The challenge of path-finding in video games is to compute optimal or near optimal paths as efficiently as possible. As both the size of the environments and the number of autonomous agents increase, this computation has to be done under hard constraints of memory and CPU resources. Hierarchical approaches, such as HNA* can compute paths more efficiently, although only for certain configurations of the hierarchy. For other configurations, performance can drop drastically when inserting the start and goal position into the hierarchy. In this paper we present improvements to HNA* to eliminate bottlenecks. We propose different methods that rely on further memory storage or parallelism on both CPU and GPU, and carry out a comparative evaluation. Results show an important speed-up for all tested configurations and scenarios.

This study presents Busy Beeway, a mobile game platform to investigate human-automation collaboration in dynamic environments. In Busy Beeway, users collaborate with automation to evade stochastically moving obstacles and reach a series of goals, in game levels of increasing difficulty. We are motivated by the need for reliable navigation aids in stochastic, dynamic environments, which are highly relevant for self-driving vehicles, UAVs, underwater and surface vehicles, and other applications. The proposed mobile game platform is agnostic to the particular algorithm underlying the autonomous system, can be used to evaluate both fully autonomous as well as human-in-the-loop systems, and is easily deployable, for large, remote user studies. This last element is key for rigorous study of human factors in navigation aids. Through a small 32--user study, we evaluate preliminary findings regarding the relative efficacy of collaborative and fully autonomous navigation, the relationship between success rate and users' learned trust in the automation (gathered via pre- and post-experiment surveys), and tolerance to error (for decisions made by the automation and by the user). This study validates the feasibility of Busy Beeway as a platform for human subject studies on human-automation collaboration, and suggests directions for future research in human-aided planning in difficult environments.

Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in a Lagrangian formulation. We model short-range and long-range collision avoidance to simulate both sparse and dense crowds. On the particles representing agents, we formulate a set of positional constraints that can be readily integrated into a standard PBD solver. We augment the tentative particle motions with planning velocities to determine the preferred velocities of agents, and project the positions onto the constraint manifold to eliminate colliding configurations. The local short-range interaction is represented with collision and frictional contact between agents, as in the discrete simulation of granular materials. We incorporate a cohesion model for modeling collective behaviors and propose a new constraint for dealing with potential future collisions. Our new method is suitable for use in interactive games.

In this study, we propose a motion generation technique which generates natural motions based on a double inverted pendulum model (DIPM) and motion capture data (Mocap). While generating the motions, the proposed controller keeps the balance of the character. A DIPM uses a hip strategy to maintain the character's stability so that the zero moment point (ZMP) stays inside the support area, composed by the feet. The naturalness of the generated motion is inherited from mocap data by aligning the motion capture sequence with the DIPM. We match the DIPM with the motion capture data in order to satisfy both the character's stability and the naturalness. To validate the proposed motion generation technique, we use two kinds of motion capture data: a balancing motion under external forces and a grasping motion with a box.

In this paper, we propose a lattice-guided human motion deformation method. Our key idea is to warp the motion space by applying an existing shape deformation technique to efficiently realize motion deformation for collision avoidance. An input motion is deformed based on the deformation of a lattice that covers the input motion. The lattice is constructed so that it covers an input motion, and it is deformed to avoid still or moving obstacles. The constraints to deform the lattice are determined based on the intersections between the vertices of the lattice that overlaps the character's body during the input motion and the space-time volume of the obstacles. Using these constraints, the lattice is deformed by applying an as-rigid-as-possible shape deformation. The joint positions of each frame pose of the input motion are altered by the lattice deformation and used to deform that frame pose. By introducing a lattice deformation, the constraints to deform a pose can be efficiently obtained while keeping space-time consistency. We evaluated our method using several motions and situations. The results show the effectiveness of our approach.

Child characters are widely used in animations and games; however, child motion capture databases are less easily available than those involving adult actors. Previous studies have shown that there is a perceivable difference in adult and child motion based on point light displays, so it may not be appropriate to just use adult motion data on child characters. Due to the costs associated with motion capture of child actors, it would be beneficial if we could create a child motion corpus by translating adult motion into child-like motion. Previous works have proposed dynamic scaling laws to transfer motion from one character to its scaled version. In this paper, we conduct a perception study to understand if this procedure can be applied to translate adult motion into child-like motion. Viewers were shown three types of point light display videos: adult motion, child motion, and dynamically scaled adult motion and asked to identify if the translated motion belongs to a child or an adult. We found that the use of dynamic scaling led to an increase in the number of people identifying the motion as belonging to a child compared to the original adult motion. Our findings suggest that although the dynamic scaling method is not a final solution to translate adult motion into child-like motion, it is nevertheless an intermediate step in the right direction. To better illustrate the original and dynamically scaled motions for the purposes of this paper, we rendered the dynamically scaled motion on an androgynous manikin character.

Inverse kinematics (IK) is a central component of systems for motion capture, character animation, motion planning, and robotics control. The field of computer graphics has developed fast stationary point solvers methods, such as the Jacobian transpose method and cyclic coordinate descent. Much work with Newton methods focus on avoiding directly computing the Hessian, and instead approximations are sought, such as in the BFGS class of solvers. This paper presents a numerical method for computing the exact Hessian of an IK system with spherical joints. It is applicable to human skeletons in computer animation applications and some, but not all, robots. Our results show that using exact Hessians can give performance advantages and higher accuracy compared to standard numerical methods used for solving IK problems. Furthermore, we provide code and supplementary details that allows researchers to plug-in exact Hessians in their own work with little effort.

In character animation, it is often the case that motions created or captured on a specific morphology need to be reused on characters having a different morphology while maintaining specific relationships such as body contacts or spatial relationships between body parts. This process, called motion retargeting, requires determining which body part relationships are important in a given animation. This paper presents a novel frame-based approach to motion retargeting which relies on a normalized representation of body joints distances. We propose to abstract postures by computing all the inter-joint distances of each animation frame and store them in Euclidean Distance Matrices (EDMs). They 1) present the benefits of capturing all the subtle relationships between body parts, 2) can be adapted through a normalization process to create a morphology-independent distance-based representation, and 3) can be used to efficiently compute retargeted joint positions best satisfying newly computed distances. We demonstrate that normalized EDMs can be efficiently applied to a different skeletal morphology by using a Distance Geometry Problem (DGP) approach, and present results on a selection of motions and skeletal morphologies. Our approach opens the door to a new formulation of motion retargeting problems, solely based on a normalized distance representation.

Floor plan designs and their spatial analysis are typically constrained to blueprints and 2D projections of 3D models. Computing appropriate spatial measures from such representations provides a standard way of quantifying important aspects of the design. We wish to investigate whether a person's perceptual exploration of a space would agree with such spatial measures, that is, whether a person can roughly infer such measures by exploring a space. We perform two studies, one involving novices and the other experts. First, we conduct a perceptual study to discover whether a novice user's perception of spatial measures depends on the mode used to explore the space. Our analysis considers three spatial measures, grounded in Space-Syntax, that characterize key aspects of a design such as visibility, accessibility, and organization. We compare three modes of exploration: 2D blueprints, first-person view in a 3D simulation, and a 3D virtual reality simulation with teleportation. A correlation analysis between the users' perceptual ratings and the spatial measures, indicates that virtual reality is the most effective of the three methods, while 2D blueprints and 3D first-person exploration often fail entirely to convey the spatial measures. In the second study, experts are asked to evaluate and rank the design blueprints for each measure. The expert observations are in strong agreement with the spatial measures for accessibility and organization, but not for visibility in some cases. This indicates that even experts have difficulty understanding spatial aspects of an architecture design from 2D blueprints alone.

We propose an automatic virtual cinematography method that takes a continuous optimization approach. A suitable camera pose or path is determined automatically by computing the minima of an objective function to obtain some desired parameters, such as those common in live action photography or cinematography. Multiple objective functions can be combined into a single optimizable function, which can be extended to model the smoothness of the optimal camera path using an active contour model. Our virtual cinematography technique can be used to find camera paths in either scripted or unscripted scenes, both with and without smoothing, at a relatively low computational cost.

Social interaction between players is an important feature in online games, where text- and voice chat is a standard way to communicate. To express emotions players can type emotes which are text-based commands to play animations from the player avatar. This paper presents a perceptual evaluation which investigates if instead expressing emotions with the face, in real-time with a web camera, is perceived more realistic and preferred in comparison to typing emote-based text commands. A user study with 24 participants was conducted where the two methods to express emotions described above were evaluated. For both methods the participants ranked the realism of facial expressions, which were based on the theory of seven universal emotions stated by American psychologist Paul Ekman: happiness, anger, fear, sadness, disgust, surprise and contempt. The participants also ranked their perceived efficiency of performing the two methods and selected the method they preferred. A significant difference was shown when analyzing the results of ranked facial expression realism. Happiness was perceived as the most realistic in both methods, while disgust and sadness were poorly rated when performed with the face. One conclusion of the perceptual evaluation was that the realism and preference between the methods showed no significant differences. However, participants had higher performance in typing with emotes. Real-time facial capture technology also needs improvements to obtain better recognition and tracking of facial features in the human face.