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Animated virtual human characters are a common feature in interactive graphical applications, such as computer and video games, online virtual worlds and simulations. Due to dynamic nature of such applications, character animation must be responsive and controllable in addition to looking as realistic and natural as possible. Though procedural and physics-based animation provide a great amount of control over motion, they still look too unnatural to be of use in all but a few specific scenarios, which is why interactive applications nowadays still rely mainly on recorded and hand-crafted motion clips. The challenge faced by animation system designers is to dynamically synthesize new, controllable motion by concatenating short motion segments into sequences of different actions or by parametrically blending clips that correspond to different variants of the same logical action. In this article, we provide an overview of research in the field of example-based motion synthesis for interactive applications. We present methods for automated creation of supporting data structures for motion synthesis and describe how they can be employed at run-time to generate motion that accurately accomplishes tasks specified by the AI or human user.

To facilitate natural interactions between humans and embodied conversational agents (ECAs), we need to endow the latter with the same nonverbal cues that humans use to communicate. Gaze cues in particular are integral in mechanisms for communication and management of attention in social interactions, which can trigger important social and cognitive processes, such as establishment of affiliation between people or learning new information. The fundamental building blocks of gaze behaviors are gaze shifts: coordinated movements of the eyes, head, and body toward objects and information in the environment. In this article, we present a novel computational model for gaze shift synthesis for ECAs that supports parametric control over coordinated eye, head, and upper body movements. We employed the model in three studies with human participants. In the first study, we validated the model by showing that participants are able to interpret the agent's gaze direction accurately. In the second and third studies, we showed that by adjusting the participation of the head and upper body in gaze shifts, we can control the strength of the attention signals conveyed, thereby strengthening or weakening their social and cognitive effects. The second study shows that manipulation of eye-head coordination in gaze enables an agent to convey more information or establish stronger affiliation with participants in a teaching task, while the third study demonstrates how manipulation of upper body coordination enables the agent to communicate increased interest in objects in the environment. ACM Reference Format:Tomislav Pejsa, Sean Andrist, Michael Gleicher, and Bilge Mutlu. 2015. Gaze and attention management for embodied conversational agents.