This paper presents Master of Puppets (MOP), an animation-by-demonstration framework that allows users to control the motion of virtual characters (puppets) in real time. In the first step, the user is asked to perform the necessary actions that correspond to the character's motions. The user's actions are recorded, and a hidden Markov model (HMM) is used to learn the temporal profile of the actions. During the runtime of the framework, the user controls the motions of the virtual character based on the specified activities. The advantage of the MOP framework is that it recognizes and follows the progress of the user's actions in real time. Based on the forward algorithm, the method predicts the evolution of the user's actions, which corresponds to the evolution of the character's motion. This method treats characters as puppets that can perform only one motion at a time. This means that combinations of motion segments (motion synthesis), as well as the interpolation of individual motion sequences, are not provided as functionalities. By implementing the framework and presenting several computer puppetry scenarios, its efficiency and flexibility in animating virtual characters is demonstrated.
This paper presents the three scripting commands and main functionalities of a novel character animation environment called CHASE. CHASE was developed for enabling inexperienced programmers, animators, artists, and students to animate in meaningful ways virtual reality characters. This is achieved by scripting simple commands within CHASE. The commands identified, which are associated with simple parameters, are responsible for generating a number of predefined motions and actions of a character. Hence, the virtual character is able to animate within a virtual environment and to interact with tasks located within it. An additional functionality of CHASE is supplied. It provides the ability to generate multiple tasks of a character, such as providing the user the ability to generate scenario-related animated sequences. However, since multiple characters may require simultaneous animation, the ability to script actions of different characters at the same time is also provided. In this paper, we propose a new method to edit captured human motion data by using the data glove. The animator first wears a glove and mimics the human body motion observed in the graphical display using his her hand. Then, a mapping function that converts the motion of the hand to that of the whole body will be generated. Finally, by moving the hand in a slightly different way, a new motion with different taste will be generated. For example, after mimicking the walking motion by alternatively moving the index finger and the middle finger, by quickly moving the fingers with larger strides, it is possible to obtain a running motion. To achieve this goal, a method to map the hand motion to the whole body is proposed. Our method can be used not only for editing human motion, but also for controlling human figures in real time environments such as games and virtual reality systems. This work aims to cover the areas of modelling and animating 3D synthetic human models at a level that is useful to students, researchers, software developers and content generators. It provides particular emphasis on facial and gesture characteristics. Articulated hand-tracking systems have been widely used in virtual reality but are rarely deployed in consumer applications due to their price and complexity. In this paper, we propose an easy-to-use and inexpensive system that facilitates 3-D articulated user-input using the hands. Our approach uses a single camera to track a hand wearing an ordinary cloth glove that is imprinted with a custom pattern. The pattern is designed to simplify the pose estimation problem, allowing us to employ a nearest-neighbor approach to track hands at interactive rates. We describe several proof-of-concept applications enabled by our system that we hope will provide a foundation for new interactions in modeling, animation control and augmented reality. It is now possible to capture the 3D motion of the human body on consumer hardware and to puppet in real time skeleton-based virtual characters. However, many characters do not have humanoid skeletons. Characters such as spiders and caterpillars do not have boned skeletons at all, and these characters have very different shapes and motions. In general, character control under arbitrary shape and motion transformations is unsolved - how might these motions be mapped? We control characters with a method which avoids the rigging-skinning pipeline - source and target characters do not have skeletons or rigs. We use interactively-defined sparse pose correspondences to learn a mapping between arbitrary 3D point source sequences and mesh target sequences. Then, we puppet the target character in real time. We demonstrate the versatility of our method through results on diverse virtual characters with different input motion controllers. Our method provides a fast, flexible, and intuitive interface for arbitrary motion mapping which provides new ways to control characters for real-time animation.  Interactively articulating virtual 3D characters lies at the heart of computer animation and geometric modeling. Expressive articulation requires control over many degrees of freedom: most often the joint angles of an internal skeleton. We introduce a physical input device assembled on the fly to control any character's skeleton directly. With traditional mouse and keyboard input, animators must rely on indirect methods such as inverse kinematics or decompose complex and integrated motions into smaller sequential manipulations---for example, iteratively positioning each bone of a skeleton hierarchy. While direct manipulation mouse and touch interfaces are successful in 2D [Shneiderman 1997], 3D interactions with 2D input are ill-posed and thus more challenging. Successful commercial products with 2D interfaces, e.g. Autodesk's Maya, have notoriously steep learning curves and require interface-specific training.    In game environments, animated character motion must rapidly adapt to changes in player input - for example, if a directional signal from the player's gamepad is not incorporated into the character's trajectory immediately, the character may blithely run off a ledge. Traditional schemes for data-driven character animation lack the split-second reactivity required for this direct control; while they can be made to work, motion artifacts will result. We describe an on-line character animation controller that assembles a motion stream from short motion fragments, choosing each fragment based on current player input and the previous fragment. By adding a simple model of player behavior we are able to improve an existing reinforcement learning method for precalculating good fragment choices. We demonstrate the efficacy of our model by comparing the animation selected by our new controller to that selected by existing methods and to the optimal selection, given knowledge of the entire path. This comparison is performed over real-world data collected from a game prototype. Finally, we provide results indicating that occasional low-quality transitions between motion segments are crucial to high-quality on-line motion generation; this is an important result for others crafting animation systems for directly-controlled characters, as it argues against the common practice of transition thresholding. This paper presents an animation system that generates an animation from natural language texts such as movie scripts or stories. It also proposes a framework for a motion database that stores numerous motion clips for various characters. We have developed semantic analysis methods to extract information for motion search and scheduling from script-like input texts. Given an input text, the system searches for an appropriate motion clip in the database for each verb in the input text. Temporal constraints between verbs are also extracted from the input text and are used to schedule the motion clips found. In addition, when necessary, certain automatic motions such as locomotion, taking an instrument, changing posture, and cooperative motions are searched for in the database. An animation is then generated using an external motion synthesis system. With our system, users can make use of existing motion clips. Moreover, because it takes natural language text as input, even novice users can use our system.  In late 2006, Nintendo released a new game controller, the Wiimote, which included a three-axis accelerometer. Since then, a large variety of novel applications for these controllers have been developed by both independent and commercial developers. We add to this growing library with three performance interfaces that allow the user to control the motion of a dynamically simulated, animated character through the motion of his or her arms, wrists, or legs. For comparison, we also implement a traditional joystick button interface. We assess these interfaces by having users test them on a set of tracks containing turns and pits. Two of the interfaces (legs and wrists) were judged to be more immersive and were better liked than the joystick button interface by our subjects. All three of the Wiimote interfaces provided better control than the joystick interface based on an analysis of the failures seen during the user study.