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arxiv: 2604.22739 · v1 · submitted 2026-04-24 · 💻 cs.CV

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Inter-Stance: A Dyadic Multimodal Corpus for Conversational Stance Analysis

Xiang Zhang , Xiaotian Li , Taoyue Wang , Nan Bi , Xin Zhou , Cody Zhou , Zoie Wang , Andrew Yang
show 4 more authors
Yuming Su Jeff Cohn Qiang Ji Lijun Yin
Authors on Pith no claims yet

Pith reviewed 2026-05-08 12:15 UTC · model grok-4.3

classification 💻 cs.CV
keywords multimodal corpusdyadic interactionstance analysisconversational stancesocial signalsphysiological signalsface videoaffect recognition
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The pith

A new 20TB multimodal corpus records 45 dyads with synchronized video, thermal, voice, physiology and stance labels to model interpersonal behavior.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the Inter-Stance corpus, a collection of synchronized recordings from 90 participants in 45 dyadic interactions. It captures 2D face video, 3D face geometry, thermal spectrum dynamics, voice and speech, multiple physiological signals, and self-reported affect, along with annotations for agreement, disagreement, and neutral stance. The data includes both pairs with shared history and strangers, collected under emotion-inducing conditions. The central aim is to provide the first public resource that combines these elements so researchers can build models of how multimodal signals convey social evaluations during conversation. A sympathetic reader would care because earlier datasets lacked this dyadic synchronization and breadth, leaving multimodal interpersonal dynamics largely inaccessible for systematic study.

Core claim

We present a new data corpus of multimodal dyadic interaction (45 dyads, 90 persons) that includes synchronized multi-modality behavior (2D face video, 3D face geometry, thermal spectrum dynamics, voice and speech behavior, physiology (PPG, EDA, heart-rate, blood pressure, and respiration), and self-reported affect of all participants in a communicative interaction scenario. Two types of dyads are included: persons with shared past history and strangers. Annotations include social signals, agreement, disagreement, and neutral stance. With a potent emotion induction, these multimodal data will enable novel modeling of multimodal interpersonal behavior. The dataset includes 20TB of multimodal

What carries the argument

The Inter-Stance dyadic corpus, a synchronized multimodal recording and annotation resource that pairs visual, thermal, audio, and physiological channels with stance labels for both familiar and unfamiliar pairs.

If this is right

  • Models can now be trained on synchronized multimodal signals to predict stance in conversations.
  • Comparisons between familiar and stranger dyads can reveal how interpersonal history shapes multimodal expression of agreement or disagreement.
  • Physiological signals can be integrated with visual and audio cues for joint analysis of affect and stance.
  • Community-wide development of social signal processing techniques becomes possible with this shared resource.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The thermal and physiological channels may allow future studies to link subconscious bodily responses to expressed stance in ways single-modality data cannot.
  • Real-time stance detection systems for video calls or virtual agents could be trained directly on the synchronized recordings.
  • Psychological research on social appraisal could combine this dyadic corpus with existing individual datasets to test how context modifies behavior.

Load-bearing premise

The collected recordings, stance annotations, and self-reports are reliable, and the full 20TB dataset will be released publicly with enough documentation for independent use by other researchers.

What would settle it

Failure to release the complete 20TB dataset with documentation, or inability of independent teams to train multimodal models that improve stance classification over single-modality baselines, would show the enabling claim does not hold.

Figures

Figures reproduced from arXiv: 2604.22739 by Andrew Yang, Cody Zhou, Jeff Cohn, Lijun Yin, Nan Bi, Qiang Ji, Taoyue Wang, Xiang Zhang, Xiaotian Li, Xin Zhou, Yuming Su, Zoie Wang.

Figure 1
Figure 1. Figure 1: One sample on our Dyadic Multimodal Corpus view at source ↗
Figure 2
Figure 2. Figure 2: Dyadic data collection system at work. Note that the view at source ↗
Figure 3
Figure 3. Figure 3: Dyads Sequence Samples of Multimodal Feature view at source ↗
Figure 4
Figure 4. Figure 4: Stance Recognition Accuracy from Physiological view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of emotion distribution across three view at source ↗
Figure 6
Figure 6. Figure 6: Facial Mimicry of Emotion by Conversational Stance view at source ↗
Figure 8
Figure 8. Figure 8: Heatmap of stance–word associations after chi-square view at source ↗
Figure 9
Figure 9. Figure 9: Self-Reported Emotional Dynamics in Dyadic Con view at source ↗
read the original abstract

Social interactions dominate our perceptions of the world and shape our daily behavior by attaching social meaning to acts as simple and spontaneous as gestures, facial expressions, voice, and speech. People mimic and otherwise respond to each other's postures, facial expressions, mannerisms, and other verbal and nonverbal behavior, and form appraisals or evaluations in the process. Yet, no publicly-available dataset includes multimodal recordings and self-report measures of multiple persons in social interaction. Dyadic recordings and annotation are lacking. We present a new data corpus of multimodal dyadic interaction (45 dyads, 90 persons) that includes synchronized multi-modality behavior (2D face video, 3D face geometry, thermal spectrum dynamics, voice and speech behavior, physiology (PPG, EDA, heart-rate, blood pressure, and respiration), and self-reported affect of all participants in a communicative interaction scenario. Two types of dyads are included: persons with shared past history and strangers. Annotations include social signals, agreement, disagreement, and neutral stance. With a potent emotion induction, these multimodal data will enable novel modeling of multimodal interpersonal behavior. We present extensive experiments to evaluate multimodal dyadic communication of dyads with and without interpersonal history, and their affect. This new database will make multimodal modeling of social interaction never possible before. The dataset includes 20TB of multimodal data to share with the research community.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper introduces Inter-Stance, a new dyadic multimodal corpus of 45 dyads (90 participants) featuring synchronized recordings of 2D/3D face video, thermal spectrum, audio/speech, and physiology (PPG, EDA, heart rate, blood pressure, respiration) plus self-reported affect. It distinguishes shared-history from stranger pairs, provides annotations for social signals and stance (agreement/disagreement/neutral), and states that extensive experiments were performed on multimodal dyadic communication and affect. The authors claim the 20 TB dataset will enable multimodal modeling of social interaction that was never possible before and will be shared publicly.

Significance. If the full dataset is released with usable documentation and the stance/affect annotations are validated, the corpus would fill a documented gap in public dyadic multimodal resources that combine multiple synchronized modalities with stance labels. This could support new work on interpersonal dynamics, multimodal fusion, and conversational analysis that existing single-person or unimodal datasets cannot address.

major comments (3)
  1. [Abstract] Abstract: the manuscript asserts that 'extensive experiments' evaluate multimodal dyadic communication of dyads with and without history, yet supplies no quantitative results, baselines, error analysis, or specific findings, leaving the utility of the corpus for the claimed novel modeling unverified.
  2. [Annotation Procedures] Annotation section: no inter-annotator agreement statistics or other reliability measures are reported for the stance labels (agreement/disagreement/neutral) or social-signal annotations, which are load-bearing for any downstream multimodal modeling claims.
  3. [Dataset Release] Dataset availability statement: although the paper states that the 20 TB corpus 'will be shared with the research community,' it provides no repository link, access protocol, metadata documentation, or licensing information required for independent use and verification.
minor comments (1)
  1. [Abstract] The title emphasizes 'Conversational Stance Analysis' while the abstract and experiments also highlight affect and emotion induction; a brief clarification of scope would reduce potential reader confusion.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the detailed and constructive review of our manuscript introducing the Inter-Stance corpus. We appreciate the referee's identification of areas where clarity and completeness can be improved. We address each major comment below and commit to revisions that strengthen the paper without altering its core contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the manuscript asserts that 'extensive experiments' evaluate multimodal dyadic communication of dyads with and without history, yet supplies no quantitative results, baselines, error analysis, or specific findings, leaving the utility of the corpus for the claimed novel modeling unverified.

    Authors: We agree that the abstract would benefit from explicitly summarizing the experimental outcomes to better substantiate the corpus's value for novel multimodal modeling. The full manuscript contains an experiments section that performs multimodal fusion analyses for stance detection and affect recognition, including comparisons between shared-history and stranger dyads. In the revised version, we will update the abstract to concisely report key quantitative results (e.g., fusion performance gains and dyad-type differences), reference the baselines employed, and note the error analysis approach. revision: yes

  2. Referee: [Annotation Procedures] Annotation section: no inter-annotator agreement statistics or other reliability measures are reported for the stance labels (agreement/disagreement/neutral) or social-signal annotations, which are load-bearing for any downstream multimodal modeling claims.

    Authors: We concur that inter-annotator agreement metrics are necessary to validate the stance and social-signal annotations. We will expand the annotation section to include these statistics (Cohen's kappa for stance labels and suitable multi-rater measures for social signals), along with details on the number of annotators, annotation guidelines, and any training or adjudication procedures used. revision: yes

  3. Referee: [Dataset Release] Dataset availability statement: although the paper states that the 20 TB corpus 'will be shared with the research community,' it provides no repository link, access protocol, metadata documentation, or licensing information required for independent use and verification.

    Authors: We acknowledge that a concrete data-release plan is required for reproducibility. We will revise the availability statement to specify the repository (with DOI), access protocol for the full 20 TB dataset, metadata documentation, and licensing (e.g., CC-BY 4.0). The release will occur upon paper acceptance to allow time for complete documentation and verification. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical data collection with no derivation chain or fitted predictions.

full rationale

The manuscript presents a new multimodal dyadic interaction corpus (45 dyads, synchronized 2D/3D face, thermal, audio, physiology, and stance/affect annotations) and describes collection/annotation procedures. No equations, models, or first-principles derivations appear; the central claim is that the released dataset will enable future modeling 'never possible before.' This is an empirical contribution whose validity hinges on public release and annotation quality, not on any self-referential reduction of predictions to inputs. No self-citations, ansatzes, or uniqueness theorems are invoked to support a closed derivation. The paper is self-contained as a data resource description and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical data-collection paper; no free parameters, mathematical axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5577 in / 1019 out tokens · 40136 ms · 2026-05-08T12:15:08.659351+00:00 · methodology

discussion (0)

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