Emotional expressivity can boost trust in human-human and humanmachine interaction. As a multimodal phenomenon, previous research argued that a mismatch in the expressive channels provides evidence of joint audio-video emotional processing. However, while previous work studied this from the point of view of emotion recognition and processing, not much is known about what effect a multimodal agent would have on a human-agent interaction task. Also, agent appearance could influence this interaction too. Here we manipulated the agent's multimodal emotional expression ("smiling face" and "smiling voice", or both) and agent type (photorealistic or cartoon-like virtual human) and assessed people's trust toward this agent. We measured trust using a mixed-methods approach, combining behavioural data from a survival task, questionnaire ratings and qualitative comments. These methods gave different results: while people commented on the importance of emotional expressivity in the agent's voice, this factor had limited influence on trusting behaviours; while people rated the cartoon-like agent on several traits higher than the photorealistic one, the agent's style also was not the most influential feature on people's trusting behaviour. These results highlight the contribution of a mixedmethods approach in human-machine interaction, as both explicit and implicit perception and behaviour will contribute to the success of the interaction.

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To simplify and accelerate the creation of blendshape rigs, using a template rig is a common procedure, especially during the creation of digital doubles. Blendshape transfer methods facilitate copy and paste functionality of the blendshapes from the template model to the digital double. However, for adequate personalization, such methods require a set of scanned training expressions of the original actor. So far, the semantics of the facial expressions to scan have been defined manually. In contrast, we formulate the semantics of the facial expressions as an integer optimization of the blendshape weights. By combining different blendshapes of the template model, our method creates facial expressions that serve as semantic references during scanning. Our method guarantees to compute as‐few‐as‐possible training expressions with minimal overlap of activated blendshapes. If the number of training expressions is limited, blendshapes are selected based on their power to personalize the resulting blendshapes compared to generic blendshape transfer methods.

Blendshape facial rigs are used extensively in the industry for facial animation of virtual humans. However, storing and manipulating large numbers of facial meshes is costly in terms of memory and computation for gaming applications, yet the relative perceptual importance of blendshapes has not yet been investigated. Research in Psychology and Neuroscience has shown that our brains process faces differently than other objects, so we postulate that the perception of facial expressions will be feature-dependent rather than based purely on the amount of movement required to make the expression. In this paper, we explore the noticeability of blendshapes under different activation levels, and present new perceptually based models to predict perceptual importance of blendshapes. The models predict visibility based on commonly-used geometry and image-based metrics. CCS CONCEPTS • Applied computing → Psychology; • Computing methodologies → Mesh geometry models; • Mathematics of computing → Equational models.

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