Animated characters that move and gesticulate appropriately with spoken text are useful in a wide range of applications. Unfortunately, this class of movement is very difficult to generate, even more so when a unique, individual movement style is required. We present a system that, with a focus on arm gestures, is capable of producing full-body gesture animation for given input text in the style of a particular performer. Our process starts with video of a person whose gesturing style we wish to animate. A tool-assisted annotation process is performed on the video, from which a statistical model of the person's particular gesturing style is built. Using this model and input text tagged with theme, rheme and focus, our generation algorithm creates a gesture script. As opposed to isolated singleton gestures, our gesture script specifies a stream of continuous gestures coordinated with speech. This script is passed to an animation system, which enhances the gesture description with additional detail. It then generates either kinematic or physically simulated motion based on this description. The system is capable of generating gesture animations for novel text that are consistent with a given performer's style, as was successfully validated in an empirical user study.

Abstract Performance has a spontaneity and “aliveness” that can be difficult to capture in more methodical animation processes such as keyframing. Access to performance animation has traditionally been limited to either low degree of freedom characters or required expensive hardware. We present a performance‐based animation system for humanoid characters that requires no special hardware, relying only on mouse and keyboard input. We deal with the problem of controlling such a high degree of freedom model with low degree of freedom input through the use of correlation maps which employ 2D mouse input to modify a set of expressively relevant character parameters. Control can be continuously varied by rapidly switching between these maps. We present flexible techniques for varying and combining these maps and a simple process for defining them. The tool is highly configurable, presenting suitable defaults for novices and supporting a high degree of customization and control for experts. Animation can be recorded on a single pass, or multiple layers can be used to increase detail. Results from a user study indicate that novices are able to produce reasonable animations within their first hour of using the system. We also show more complicated results for walking and a standing character that gestures and dances.

Abstract We present a novel approach to create plausible 3D face models from vague recollections or incomplete descriptions. This task plays an important role in police work, where composite facial images of suspects need to be created from vague descriptions given by the eyewitnesses of an incident. Our approach is based on a morphable model of 3D faces and takes into account correlations among facial features based on human anatomy and ethnicity. Using these correlations, unspecified parts of the target face are automatically completed to yield a coherent face model. The system uses a novel paradigm for navigating face space and provides high‐level control of facial attributes as well as the possibility to import facial features from a database. In addition, the user can specify a set of attribute constraints that are used to restrict the target face to a residual subspace. These constraints can also be enforced on the example faces in the database, bringing their appearance closer to the mental image of the user, and thus avoiding confusing exposure to entirely different faces. We also propose a novel approach for adapting the system to local populations based on additional image databases that are converted into our 3D representation by automated shape reconstruction. We demonstrate the applicability of our system in a simulated forensic scenario and compare our results with those obtained by a professional forensic artist using state‐of‐the‐art software for creating composite images in police work. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques I.4.10 [Image Processing and Computer Vision]: Image Representation—Hierarchical, Multidimensional, Statistical J.m [Computer Applications]: Miscellaneous—Forensic Sciences

Athletes and coaches in most professional sports make use of high-tech equipment to analyze and, subsequently, improve the athlete's performance. High-speed video cameras are employed, for instance, to record the swing of a golf club or a tennis racket, the movement of the feet while running, and the body motion in apparatus gymnastics. High-tech and high-speed equipment, however, usually implies high-cost as well. In this paper, we present a passive optical approach to capture high-speed motion using multi-exposure images obtained with low-cost commodity still cameras and a stroboscope. The recorded motion remains completely undisturbed by the motion capture process. We apply our approach to capture the motion of hand and ball for a variety of baseball pitches and present algorithms to automatically track the position, velocity, rotation axis, and spin of the ball along its trajectory. To demonstrate the validity of our setup and algorithms, we analyze the consistency of our measurements with a physically based model that predicts the trajectory of a spinning baseball. Our approach can be applied to capture a wide variety of other high-speed objects and activities such as golfing, bowling, or tennis for visualization as well as analysis purposes.

We introduce a facial animation system that produces real-time animation sequences including speech synchronization and non-verbal speech-related facial expressions from plain text input. A state-of-the-art text-to-speech synthesis component performs linguistic analysis of the text input and creates a speech signal from phonetic and intonation information. The phonetic transcription is additionally used to drive a speech synchronization method for the physically based facial animation. Further high-level information from the linguistic analysis such as different types of accents or pauses as well as the type of the sentence is used to generate non-verbal speech-related facial expressions such as movement of head, eyes, and eyebrows or voluntary eye blinks. Moreover, emotions are translated into XML markup that triggers emotional facial expressions.