Abstract In recent years, the fashion apparel industry has been increasingly employing virtual simulations for the development of new products. The first step in virtual garment simulation involves identifying the optimal simulation parameters that accurately reproduce the drape properties of the actual fabric. Recent techniques advocate for a data‐driven approach, estimating parameters from outcomes of a Cusick drape test. Such methods deviate from standard Cusick drape tests, introducing high‐cost tools, which reduces practicality. Our research presents a more practical model, utilizing 2D silhouette images from the ISO‐standardized Cusick drape test. Notably, while past models have shown limitations in estimating stretching parameters, our novel approach leverages the fabric's tag information including fabric type and fiber composition. Our proposed model functions as a cascaded system: first, it estimates stretching parameters using tag information, then, in the subsequent step, it considers the estimated stretching parameters alongside the fabric sample's Cusick drape test results to determine bending parameters. We validated our model against existing methods and applied it in practical scenarios, showing promising outcomes. (see https://www.acm.org/publications/class-2012 )

Abstract We present a cloth simulation parameter estimation method that integrates the flexibility of global optimization with the speed of neural networks. While global optimization allows for varied designs in objective functions and specifying the range of optimization variables, it requires thousands of objective function evaluations. Each evaluation, which involves a cloth simulation, is computationally demanding and impractical time‐wise. On the other hand, neural network learning methods offer quick estimation results but face challenges such as the need for data collection, re‐training when input data formats change, and difficulties in setting constraints on variable ranges. Our proposed method addresses these issues by replacing the simulation process, typically necessary for objective function evaluations in global optimization, with a neural network for inference. We demonstrate that, once an estimation model is trained, optimization for various objective functions becomes straightforward. Moreover, we illustrate that it is possible to achieve optimization results that reflect the intentions of expert users through visualization of a wide optimization space and the use of range constraints.

Abstract How similar a virtual product is to a real product is one of the most important issues when using virtual simulation to develop real apparel designs. The first step to achieve high similarity is finding optimal simulation parameters for the desired fabrics. However, it is notoriously difficult to find an optimal parameter set that reproduces the physical properties of a specific fabric as closely as possible. It is because the relationship between the changes of simulation parameters and drape shapes is highly non‐linear, not intuitive, and hard to be predicted even by experts. Therefore, users have to repeat trial and error based on personal experience until they find satisfactory results, which is time consuming due to the simulation time required for each trial. To handle this problem, we proposed a neural network model that learns the relationship between the parameter space and the drape space, then we presented a user interface that allows users to quickly explore the extensive drape space through simulation parameters. To validate our method, we provided our UI with experts in the fashion design industry and conducted user studies with them for qualitative evaluation.

Abstract Direct manipulation can be used as an intuitive interface for spatial interaction with multiple objects in virtual environments. However, users often have difficulties in precisely manipulating objects in the 3D space; this is mainly because the user is not provided with enough visual information to sufficiently recognize the spatial relationships among objects. We present a new method for improving the preciseness of object manipulation in the 3D space by providing the user with an auxiliary view that is consistently retargeted to closely highlight the spatial relationship between the manipulated object and the object nearest to it. Both the target and the position of the auxiliary camera are computed efficiently using the bisector surface of the two involved objects. The viewing target is set to the center of the closest area between the two objects, and the camera position is optimized under an objective function primarily measuring the visual clarity of the auxiliary view. We demonstrate the usefulness of our method through various experiments based on practical usage scenarios.

In this paper, we propose a novel approach for the classification and retrieval of interactions between human characters and objects. We propose to use the interaction bisector surface (IBS) between the body and the object as a feature of the interaction. We define a multi-resolution representation of the body structure, and compute a correspondence matrix hierarchy that describes which parts of the character's skeleton take part in the composition of the IBS and how much they contribute to the interaction. Key-frames of the interactions are extracted based on the evolution of the IBS and used to align the query interaction with the interaction in the database. Through the experimental results, we show that our approach outperforms existing techniques in motion classification and retrieval, which implies that the contextual information plays a significant role for scene and interaction description. Our method also shows better performance than other techniques that use features based on the spatial relations between the body parts, or the body parts and the object. Our method can be applied for character motion synthesis and robot motion planning.

Abstract We present a points‐based user interface for character posing. In our method, users insert a number of three‐dimensional (3D) points in a virtual environment. The system performs a linear search of a motion capture database for the best matched pose and then places the pose immediately in the virtual environment to be overlapped with the input points. For a fast and precise distance computation between the input points and the example poses from the database, we developed a closed‐form solution of the 3D points registration problem. To demonstrate the easiness and usability of our approach, we built a motion database including various kinds of human motion and conducted a user study of character posing tasks with non‐expert users. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract We present a method for the interactive control of big‐object manipulation animation. When a human character holds a big arbitrarily shaped object, it should use the whole upper body, including both arms and the torso in a limited range such that the holding postures can be physically as well as visually believable to users. In our method, we first sample various postures that satisfy our feasibility conditions: collision free and physically stable. The concept of the wrench space is used to evaluate the physical stability of holding postures. Then, we build a graph structure in which the nodes are the sampled postures and the links are collision‐free motion interpolating between two‐sampled postures. This manipulation motion graph allows us to use a general pathfinding method, such as Dijkstra's algorithm, to achieve real‐time controllability. In our experiments, we build the manipulation motion graphs for differently shaped objects, and we demonstrate the usability of our method via several examples in which users interactively control the animation through the target positions or desired trajectories of the objects. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract We present an interactive method for creating animation sequences of characters based on captured motion data in an exploratory way as in assembling construction toys. The key component of our method is a path browser that can retrieve and visualize paths as diverse as possible connecting a given pair of initial and final motion fragments instantiated in the space. With the aid of our path browser, the user can develop large‐scale assembly of motions gradually through iterations of arranging and putting together motion fragments. For the efficient retrieval of connecting paths, we use a bidirectional search tree that grows from the initial and final configurations simultaneously under the guidance of a mixed strategy for both global exploration and local optimization. The usefulness of our approach is demonstrated through experiments with a variety of motion data including box moving, basketball, and breakdance data. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Image storyboards of films and videos are useful for quick browsing and automatic video processing. A common approach for producing image storyboards is to display a set of selected key‐frames in temporal order, which has been widely used for 2D video data. However, such an approach cannot be applied for 3D animation data because different information is revealed by changing parameters such as the viewing angle and the duration of the animation. Also, the interests of the viewer may be different from person to person. As a result, it is difficult to draw a single image that perfectly abstracts the entire 3D animation data. In this paper, we propose a system that allows users to interactively browse an animation and produce a comic sequence out of it. Each snapshot in the comic optimally visualizes a duration of the original animation, taking into account the geometry and motion of the characters and objects in the scene. This is achieved by a novel algorithm that automatically produces a hierarchy of snapshots from the input animation. Our user interface allows users to arrange the snapshots according to the complexity of the movements by the characters and objects, the duration of the animation and the page area to visualize the comic sequence. Our system is useful for quickly browsing through a large amount of animation data and semi‐automatically synthesizing a storyboard from a long sequence of animation.

ABSTRACT Capturing a close interaction between an actor and an object can be difficult as a result of occlusion and having to recreate the geometry of the scene accurately. In this paper, we propose a technique that allows us to capture the object's motion and geometry alongside the actor's movements and optionally the local environment, using a magnetic motion capture system and an RGB‐D sensor. This not only gives greater information when placing a character in a scene but enables us to digitally recreate the scene in motion without significant animator work after capture. The use of magnetic sensors prevents occlusion or marker confusion that is common in optical techniques when dealing with close interactions, as the magnetic sensors do not require direct line of sight to a camera. The geometry reconstruction ensures that the proportions of objects and surfaces the character interacts with are accurate and alleviates the need for an artist to model the object. We perform validation of the results by comparison with an optical system and show a variety of motions, such as using a screwdriver or removing a cap to drink from a bottle, that can be captured using our technique. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract We propose 2D stick figures as a unified medium for visualizing and searching for human motion data. The stick figures can express a wide range or human motion, and they are easy to be drawn by people without any professional training. In our interface, the user can browse overall motion by viewing the stick figure images generated from the database and retrieve them directly by using sketched stick figures as an input query. We started with a preliminary survey to observe how people draw stick figures. Based on the rules observed from the user study, we developed an algorithm converting motion data to a sequence of stick figures. The feature‐based comparison method between the stick figures provides an interactive and progressive search for the users. They assist the user's sketching by showing the current retrieval result at each stroke. We demonstrate the utility of the system with a user study, in which the participants retrieved example motion segments from the database with 102 motion files by using our interface.

Abstract We present an interactive method that allows animated characters to navigate through cluttered environments. Our characters are equipped with a variety of motion skills to clear obstacles, narrow passages, and highly constrained environment features. Our control method incorporates a behavior model into well‐known, standard path planning algorithms. Our behavior model, calleddeformable motion, consists of a graph of motion capture fragments. The key idea of our approach is to add flexibility on motion fragments such that we can situate them into a cluttered environment via constraint‐based formulation. We demonstrate our deformable motion for realtime interactive navigation and global path planning in highly constrained virtual environments.

Crowd simulation has been an important research field due to its diverse range of applications that include film production, military simulation, and urban planning. A challenging problem is to provide simple yet effective control over captured and simulated crowds to synthesize intended group motions. We present a new method that blends existing crowd data to generate a new crowd animation. The new animation can include an arbitrary number of agents, extends for an arbitrary duration, and yields a natural-looking mixture of the input crowd data. The main benefit of this approach is to create new spatio-temporal crowd behavior in an intuitive and predictable manner. It is accomplished by introducing a morphable crowd model that allows us to encode the formations and individual trajectories in crowd data. Then, its original spatio-temporal behavior can be reconstructed and interpolated at an arbitrary scale using our morphable model.

Abstract The standard C/C++ implementation of a spatial partitioning data structure, such as octree and quadtree, is often inefficient in terms of storage requirements particularly when the memory overhead for maintaining parent‐to‐child pointers is significant with respect to the amount of actual data in each tree node. In this work, we present a novel data structure that implements uniform spatial partitioning without storing explicit parent‐to‐child pointer links. Our linkless tree encodes the storage locations of subdivided nodes using perfect hashing while retaining important properties of uniform spatial partitioning trees, such as coarse‐to‐fine hierarchical representation, efficient storage usage, and efficient random accessibility. We demonstrate the performance of our linkless trees using image compression and path planning examples.

Real time animation of human figures in virtual environments is an important problem in the context of computer games and virtual environments. Recently, the use of large collections of captured motion data has increased realism in character animation. However, assuming that the virtual environment is large and complex, the effort of capturing motion data in a physical environment and adapting them to an extended virtual environment is the bottleneck for achieving interactive character animation and control. We present a new technique for allowing our animated characters to navigate through a large virtual environment, which is constructed using a set of building blocks. The building blocks, called motion patches , can be arbitrarily assembled to create novel environments. Each patch is annotated with motion data, which informs what actions are available for animated characters within the block. The versatility and flexibility of our approach are demonstrated through examples in which multiple characters are animated and controlled at interactive rates in large, complex virtual environments.