A person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: ‘The face is the portrait of the mind; the eyes, its informers’. This presents a significant ...challenge for Computer Graphics researchers who generate artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human–human interactions. This review article provides an overview of the efforts made on tackling this demanding task. As with many topics in computer graphics, a cross‐disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We begin with a discussion of the movement of the eyeballs, eyelids and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Furthermore, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye gaze, during the expression of emotion or during conversation. We discuss how these findings are synthesized in computer graphics and can be utilized in the domains of Human–Robot Interaction and Human–Computer Interaction for allowing humans to interact with virtual agents and other artificial entities. We conclude with a summary of guidelines for animating the eye and head from the perspective of a character animator.
A person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: ‘The face is the portrait of the mind; the eyes, its informers’. This presents a significant challenge for Computer Graphics researchers who generate artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human–human interactions. This review article provides an overview of the efforts made on tackling this demanding task. As with many topics in computer graphics, a cross‐disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user.
Eyes alive Lee, Sooha Park; Badler, Jeremy B.; Badler, Norman I.
ACM transactions on graphics,
07/2002, Letnik:
21, Številka:
3
Journal Article
Recenzirano
For an animated human face model to appear natural it should produce eye movements consistent with human ocular behavior. During face-to-face conversational interactions, eyes exhibit conversational ...turn-taking and agent thought processes through gaze direction, saccades, and scan patterns. We have implemented an eye movement model based on empirical models of saccades and statistical models of eye-tracking data. Face animations using stationary eyes, eyes with random saccades only, and eyes with statistically derived saccades are compared, to evaluate whether they appear natural and effective while communicating.
Smooth pursuit eye movements are guided largely by retinal-image motion. To compensate for neural conduction delays, the brain employs a predictive mechanism to generate anticipatory pursuit that ...precedes target motion (E. Kowler, 1990). A critical question for interpreting neural signals recorded during pursuit concerns how this mechanism is interfaced with sensorimotor processing. It has been shown that the predictor is not simply turned-off during randomization because anticipatory eye velocity remains when target velocity is randomized (E. Kowler & S. McKee, 1987; G. W. Kao & M. J. Morrow, 1994). This study was completed to compare pursuit behavior during randomized motion-onset timing with that occurring during direction or speed randomization. We found that anticipatory eye velocity persisted despite motion-onset randomization, and that anticipation onset time was between that observed in the different constant-timing conditions. This centering strategy was similar to the bias of eye velocity magnitude away from extremes observed when direction or speed was randomized. Such a strategy is comparable to least-squares error minimization, and could be used to facilitate acquisition of a target when it begins to move. Centering was in some observers accounted for by a shift of eye velocity toward that generated in the preceding trial. The results make unlikely a model in which the predictor is disengaged by randomizing stimulus timing, and suggest that predictive signals always interact with those used in sensorimotor processing during smooth pursuit.
Animals often make anticipatory movements to compensate for slow reaction times. Anticipatory movements can be timed using external, sensory cues, or by an internal prediction of when an event will ...occur. However, it is unknown whether external or internal cues dominate the anticipatory response when both are present. Smooth pursuit eye movements are generated by a motor system heavily influenced by anticipation. We measured pursuit to determine how its timing was influenced when both a predictable event and a visual cue were present. Monkeys tracked a moving target that appeared at a constant time relative to the onset of a fixation point. At a randomized time before target onset, the fixation point disappeared, creating a temporal "gap" that cued impending target motion. We found that the gap onset cue and prediction of target onset together determined pursuit initiation time. We also investigated whether prediction could override the gap onset cue or vice versa by manipulating target onset and, hence, the duration of time that the animal had to estimate to predict it. When target motion began earlier, the pursuit system relied more on prediction to trigger a movement, whereas the cue was more often used when the target moved later. Pursuit latency in previous trials partially accounted for this behavior. The results suggest that neither internal nor external factors dominate to control the anticipatory response and that the relative contributions vary with stimulus conditions. A model in which neuronal anticipation and fixation signals interact can explain the results.
Fixating a small dot is a universal technique for stabilizing gaze in vision and eye movement research, and for clinical imaging of normal and diseased retinae. During fixation, microsaccades and ...drifts occur that presumably benefit vision, yet microsaccades compromise image stability and usurp task attention. Previous work suggested that microsaccades and smooth pursuit catch-up saccades are controlled by similar mechanisms. This, and other previous work showing fewer catch-up saccades during smooth pursuit of peripheral targets suggested that a peripheral target might similarly mitigate microsaccades. Here, human observers fixated one of three stimuli: a small central dot, the center of a peripheral, circular array of small dots, or a central/peripheral stimulus created by combining the two. The microsaccade rate was significantly lower with the peripheral array than with the dot. However, inserting the dot into the array increased the microsaccade rate to single-dot levels. Drift speed also decreased with the peripheral array, both with and without the central dot. Eye position variability was higher with the array than with the composite stimulus. The results suggest that analogous to the foveal pursuit, foveating a stationary target engages the saccadic system likely compromising retinal-image stability. In contrast, fixating a peripheral stimulus improves stability, thereby affording better retinal imaging and releasing attention for experimental tasks.
When two objects such as billiard balls collide, observers perceive that the action of one caused the motion of the other. We have previously shown (Badler, Lefèvre, & Missal, 2010) that this extends ...to the oculomotor domain: subjects make more predictive movements in the expected direction of causal motion than in a noncausal direction. However, predictive oculomotor and reactive psychophysical responses have never been directly compared. They should be correlated if they tap into the same mental processes. To test this, we recorded oculomotor responses to launching stimuli, then asked subjects to manually classify those stimuli as causal or noncausal. Overall the psychophysical classifications matched the oculomotor biases, although correlations across subjects were mostly absent. In subsequent experiments, 50% of the trials had a 300-millisecond delay after the collision to impede the perception of causality. Subjects maintained their causal oculomotor bias but used different classification strategies, usually grouping the stimuli either by delay or by direction. In addition, there was no evidence that the two response types were correlated on a trial-by-trial basis. The results suggest divergent processes underlying oculomotor responses to and judgments of causal stimuli.
Good performance in the sport of baseball shows that humans can determine the trajectory of a moving object and act on it under the constraint of a rule. We report here on neuronal activity in the ...supplementary eye field (SEF) of monkeys performing an eye movement task inspired by baseball. In "ocular baseball," a pursuit eye movement to a target is executed or withheld based on the target's trajectory. We found that a subset of neurons in the SEF interpreted the trajectory according to the task rule. Other neurons specified at a later time the command to pursue the target with the eyes. The results suggest that the SEF can interpret sensory signals about target motion in the context of a rule to guide voluntary eye movement initiation.
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