The Influence of Emotion-based Prioritized Facial Expressions on Social Presence in Avatar-mediated Remote Communication
Bibliographic record
Abstract
In avatar-mediated remote communication, avatars’ facial expressions can be dynamically adjusted according to each user’s computational and device constraints, highlighting the importance of varied expressions and their impact on user perception. However, there is a lack of research on how variations in avatar facial expressions, especially when simplified, influence user perception, particularly in terms of social presence. To address this, we examine the impact of various facial expression combinations on social presence in avatar-mediated communication scenarios, ranging from informative speeches to emotional conversations. Our approach involves prioritizing avatar facial blendshape combinations using two main approaches: (1) commonly activated expressions that reflect the active facial movements observed during casual conversations, and (2) emotion-based expressions derived from Facial Action Coding System (FACS). These combinations were compared against minimal baseline and full blendshape conditions through a comprehensive study involving 32 participants. Our findings reveal that emotion-based condition achieves comparable levels of social presence and communication quality to the full condition, in both informative speeches and emotional conversations. This highlights the effectiveness of prioritizing emotion-based expressions and adopting a streamlined approach to avatar facial control. By focusing on emotional expressions while optimizing resources, this approach shows potential for enhancing the avatar-mediated communication experience, accommodating the diverse users’ contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".