Seeing is Believing: Body Motion Dominates in Multisensory Conversations
Bibliographic record
Abstract
In many scenes with human characters, interacting groups are an important factor for maintaining a sense of realism. However, little is known about what makes these characters appear realistic. In this paper, we investigate human sensitivity to audio mismatches (i.e., when individuals’ voices are not matched to their gestures) and visual desynchronization (i.e., when the body motions of the individuals in a group are mis-aligned in time) in virtual human conversers. Using motion capture data from a range of both polite conversations and arguments, we conduct a series of perceptual experiments and determine some factors that contribute to the plausibility of virtual conversing groups. We found that participants are more sensitive to visual desynchronization of body motions, than to mismatches between the characters’ gestures and their voices. Furthermore, synthetic conversations can appear sufficiently realistic once there is an appropriate balance between talker and listener roles. This is regardless of body motion desynchronization or mismatched audio.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".