Artificial intelligence researchers attempting to create engaging, apparently living creatures may find important insight in the work of artists who have explored the idea of believable character. In particular, appropriately timed and clearly expressed emotion is a central requirement for believable characters. We discuss these ideas and suggest how they may apply to believable interactive characters, which we call "believable agents." This work was supported in part by Fujitsu Laboratories and Mitsubishi Electric Research Laboratories. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any other parties.

This article describes a process that greatly expands the range of possible motions. Mixing motions selected from a database lets you create a newmotion to exact specifications. The synthesized motion retains the original motions' subtle qualities, such as the realism of motion capture or the expressive, exaggerated qualities of artistic animation. Ourmethod provides a newway to achieve inverse kinematics capability---for example, placing the hands or feet of an articulated figure in specific positions. It proves useful for both real-time graphics and prerendered animation production.

Human movements include limb gestures and postural attitude. Although many computer animation researchers have studied these classes of movements, procedurally generated movements still lack naturalness. We argue that looking only at the psychological notion of gesture is insufficient to capture movement qualities needed by animated characters. We advocate that the domain of movement observation science, specifically Laban Movement Analysis (LMA) and its Effort and Shape components, provides us with valuable parameters for the form and execution of qualitative aspects of movements. Inspired by some tenets shared among LMA proponents, we also point out that Effort and Shape phrasing across movements and the engagement of the whole body are essential aspects to be considered in the search for naturalness in procedurally generated gestures. Finally, we present EMOTE (Expressive MOTion Engine), a 3D character animation system that applies Effort and Shape qualities to independently defined underlying movements and thereby generates more natural synthetic gestures.

User interfaces are often based on static presentations, a model ill suited for conveying change. Consequently, events on the screen frequently startle and confuse users. Cartoon animation, in contrast, is exceedingly successful at engaging its audience; even the most bizarre events are easily comprehended. The Self user interface has served as a testbed for the application of cartoon animation techniques as a means of making the interface easier to understand and more pleasant to use. Attention to timing and transient detail allows Self objects to move solidly. Use of cartoon-style motion blur allows Self objects to move quickly and still maintain their comprehensibility. Self objects arrive and depart smoothly, without sudden materializations and disappearances, and they rise to the front of overlapping objects smoothly through the use of dissolve. Anticipating motion with a small contrary motion and pacing the middle of transitions faster than the endpoints results in smoother and c...

“We’re searching here, trying to get away from the cut and dried handling of things all the way through—everything—and the only way to do it is to leave things open until we have completely explored every bit of it.” –Walt Disney Researchers in nonphotorealistic rendering (NPR) have investigated a variety of techniques to simulate the styles of artists. Recent work has resulted in methods for pen-and-ink illustration, pencil sketching, watercolor, engraving, and silhouette edge rendering. This paper presents real-time methods to emulate cartoon styles. We also present variations on a texture mapping technique to achieve real-time pencil sketching. We demonstrate our method of inking silhouettes, material and mesh boundaries, and crease edges. In addition, we present techniques for emphasizing motion of cartoon objects by introducing geometry into the cartoon scene. The rendering system is integrated with an animation system and a runtime multiresolution mesh (MRM) system to achieve scalability, ensuring real-time performance on any platform. Such solutions allow us to take advantage of evolving hardware in order to make nonphotorealistic animation and rendering achievable on low- and high-end consumer platforms. All of the techniques described can be applied to models created with standard modeling tools and require no additional mark-up information from the modeler.

Animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computationally demanding. Likewise, finding controllers that enable physics-based models to produce desired animations usually entails formidable computational cost. This paper demonstrates the possibility of replacing the numerical simulation and control of model dynamics with a dramatically more efficient alternative. In particular, we propose the NeuroAnimator, a novel approach to creating physically realistic animation that exploits neural networks. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physics-based models in action. Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation. Furthermore, by exploiting the network structure of the NeuroAnimator, we introduce a fast algorithm for learning controllers that enables either physics-based models or their neural network emulators to synthesize motions satisfying prescribed animation goals. We demonstrate NeuroAnimators for passive and active (actuated) rigid body, articulated, and deformable physics-based models.

Animated pedagogical agents offer great promise for learning environments. In addition to their significant motivational benefits, these intriguing lifelike characters can also play an important pedagogical role. Introduced into a rich learning environment, animated pedagogical agents could increase problem-solving effectiveness by providing students with customized advice. To evaluate the effects of animated pedagogical agents on students' problem solving, we conducted a large-scale formal study with 100 middle school students interacting with an animated pedagogical agent that inhabits Design-A-Plant, a design-centered learning environment for botanical anatomy and physiology. The lifelike agent advises students as they design plants to survive in didderent environmental conditions. In the study, students interacted with one of dive didderent clones of the agent. The results of the study demonstrate that animated pedagogical agents can yield important educational benedits in the form of improved problem solving, particularly for complex problems. They also show that animated pedagogical agents that provide multiple levels of advice with multiple modalities yield greater improvements in problem solving than less expressive animated pedagogical agents.

Since it relies on a geometrical rather than a variational framework, many find the finite volume method (FVM) more intuitive than the finite element method (FEM). We show that the FVM allows one to interpret the stress inside a tetrahedron as a simple “multidimensional force ” pushing on each face. Moreover, this interpretation leads to a heuristic method for calculating the force on each node, which is as simple to implement and comprehend as masses and springs. In the finite volume spirit, we also present a geometric rather than interpolating function definition of strain. We use the FVM and a quasi-incompressible, transversely isotropic, hyperelastic constitutive model to simulate contracting muscle tissue. B-spline solids are used to model fiber directions, and the muscle activation levels are derived from key frame animations.

Today's computer animators have access to many systems and techniques to author high quality motion. Unfortunately, available techniques typically produce a particular motion for a specific character. In this paper, we present a constraintbased approach to adapt previously created motions to new situations and characters. We combine constraint methods that compute changes to motion to meet specified needs, with motion-signal processing methods that modify signals yet preserve desired properties. This allows the adaptation of motions to meet new goals while retaining much of their original quality.

This paper introduces Gesture Engine, an animation system that synthesizes human gesturing behaviors from augmented conversation transcripts using a database of highlevel gesture definitions. An abstract scripting language to specify hand-arm gestures is introduced that incorporates knowledge from sign language research, psycholinguistics, and traditional keyframe animation. A new planning algorithm instantiates and adjusts gestures according to communicative context and temporal constraints obtained from a speech synthesizer. The system animates an MPEG-4 compliant skeleton using Body Animation Parameters. 1