Perceptual integration of kinematic components in the recognition of emotional facial expressions

Chiovetto, Enrico; Curio, C; Endres, Dominik; Giese, Martin A.

doi:10.1167/18.4.13

Cited by 13 publications

(9 citation statements)

References 63 publications

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“…In an experiment similar to ours, it has been shown that hierarchical Gaussian process dynamical models can synthesize hand shake movements indistinguishable from natural ones (Taubert et al 2012). Furthermore, the perception of emotion based on spatio-temporal MPs has been investigated by Roether et al (2009) and Chiovetto et al (2018). In our study, we are interested in comparing different MP types in a unified Bayesian framework (Endres et al 2013) with respect to the perception of naturalness.…”

Section: Related Workmentioning

confidence: 84%

Predicting Perceived Naturalness of Human Animations Based on Generative Movement Primitive Models

Knopp

Velychko

Dreibrodt

et al. 2019

ACM Trans. Appl. Percept.

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We compared the perceptual validity of human avatar walking animations driven by six different representations of human movement using a graphics Turing test. All six representations are based on movement primitives (MPs), which are predictive models of full-body movement that differ in their complexity and prediction mechanism. Assuming that humans are experts at perceiving biological movement from noisy sensory signals, it follows that these percepts should be describable by a suitably constructed Bayesian ideal observer model. We build such models from MPs and investigate if the perceived naturalness of human animations are predictable from approximate Bayesian model scores of the MPs. We found that certain MP-based representations are capable of producing movements that are perceptually indistinguishable from natural movements. Furthermore, approximate Bayesian model scores of these representations can be used to predict perceived naturalness. In particular, we could show that movement dynamics are more important for perceived naturalness of human animations than single frame poses. This indicates that perception of human animations is highly sensitive to their temporal coherence. More generally, our results add evidence for a shared MP-representation of action and perception. Even though the motivation of our work is primarily drawn from neuroscience, we expect that our results will be applicable in virtual and augmented reality settings, when perceptually plausible human avatar movements are required.

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Section: Related Workmentioning

confidence: 84%

Predicting Perceived Naturalness of Human Animations Based on Generative Movement Primitive Models

Knopp

Velychko

Dreibrodt

et al. 2019

ACM Trans. Appl. Percept.

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“…How many dimensions are used to encode the full space of facial motions, and what are these dimensions? Recent evidence suggests that a small number of dimensions are sufficient (Dobs et al, 2014 ; Chiovetto et al, 2018 ) but more studies based on larger data sets are needed. If a set of basic components can be characterized, can we identify behavioral and neural correlates of a facial motion space, similar to what is known as face space for static faces (Valentine, 2001 ; Leopold et al, 2006 ; Chang and Tsao, 2017 )?…”

Section: Discussionmentioning

confidence: 99%

Use and Usefulness of Dynamic Face Stimuli for Face Perception Studies—a Review of Behavioral Findings and Methodology

2018

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Faces that move contain rich information about facial form, such as facial features and their configuration, alongside the motion of those features. During social interactions, humans constantly decode and integrate these cues. To fully understand human face perception, it is important to investigate what information dynamic faces convey and how the human visual system extracts and processes information from this visual input. However, partly due to the difficulty of designing well-controlled dynamic face stimuli, many face perception studies still rely on static faces as stimuli. Here, we focus on evidence demonstrating the usefulness of dynamic faces as stimuli, and evaluate different types of dynamic face stimuli to study face perception. Studies based on dynamic face stimuli revealed a high sensitivity of the human visual system to natural facial motion and consistently reported dynamic advantages when static face information is insufficient for the task. These findings support the hypothesis that the human perceptual system integrates sensory cues for robust perception. In the present paper, we review the different types of dynamic face stimuli used in these studies, and assess their usefulness for several research questions. Natural videos of faces are ecological stimuli but provide limited control of facial form and motion. Point-light faces allow for good control of facial motion but are highly unnatural. Image-based morphing is a way to achieve control over facial motion while preserving the natural facial form. Synthetic facial animations allow separation of facial form and motion to study aspects such as identity-from-motion. While synthetic faces are less natural than videos of faces, recent advances in photo-realistic rendering may close this gap and provide naturalistic stimuli with full control over facial motion. We believe that many open questions, such as what dynamic advantages exist beyond emotion and identity recognition and which dynamic aspects drive these advantages, can be addressed adequately with different types of stimuli and will improve our understanding of face perception in more ecological settings.

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“…While this previous work focused on the perception of naturalness of movements, the current study addresses the predictive mechanism inherent in MP models. Similar studies were also conducted for MP models of emotional handshakes [Taubert et al 2012] and facial expressions [Chiovetto et al 2018].…”

Section: Related Workmentioning

confidence: 82%

Evaluating Perceptual Predictions based on Movement Primitive Models in VR- and Online-Experiments

Knopp

Velychko

Dreibrodt

et al. 2020

ACM Symposium on Applied Perception 2020

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We investigate the role of prediction in biological movement perception by comparing different representations of human movement in a virtual reality (VR) and online experiment. Predicting movement enables quick and appropriate action by both humans and artificial agents in many situations, e.g. when the interception of objects is important. We use different predictive movement primitive (MP) models to probe the visual system for the employed prediction mechanism. We hypothesize that MP-models, originally devised to address the degrees-of-freedom (DOF) problem in motor production, might be used for perception as well. In our study we consider object passing movements. Our paradigm is a predictive task, where participants need to discriminate movement continuations generated by MP models from the ground truth of the natural continuation. This experiment was conducted first in VR, and later on continued as online experiment. We found that results transfer from the controlled and immersive VR setting with movements rendered as realistic avatars to a simple and COVID-19 safe online setting with movements rendered as stick figures. In the online setting we further investigate the effect of different occlusion timings. We found that contact events during the movement might provide segmentation points that render the lead-in movement independent of the continuation and thereby make perceptual predictions much harder for subjects. We compare different MP-models by their capability to produce perceptually believable movement continuations and their usefulness to predict this perceptual naturalness. Our research might provide useful insight for application in computer animation, by showing how movements can be continued without violating the expectation of the user. Our results also contribute towards an efficient method of animating avatars by combining simple movements into complex movement sequences.

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Perceptual integration of kinematic components in the recognition of emotional facial expressions

Cited by 13 publications

References 63 publications

Predicting Perceived Naturalness of Human Animations Based on Generative Movement Primitive Models

Predicting Perceived Naturalness of Human Animations Based on Generative Movement Primitive Models

Use and Usefulness of Dynamic Face Stimuli for Face Perception Studies—a Review of Behavioral Findings and Methodology

Evaluating Perceptual Predictions based on Movement Primitive Models in VR- and Online-Experiments

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