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arxiv: 2606.07935 · v1 · pith:AGQKAZYYnew · submitted 2026-06-06 · 💻 cs.CV

REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Prediction

Siyang Song , Micol Spitale , Zijian Wu , Xiangyu Kong , Cheng Luo , Cristina Palmero , German Barquero , Sergio Escalera
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Michel Valstar Mohamed Daoudi Fabien Ringeval Andrew Howes Elisabeth Andre Hatice Gunes
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Pith reviewed 2026-06-27 20:25 UTC · model grok-4.3

classification 💻 cs.CV
keywords facial reaction generationpersonalized MAFRGEEG recordingsBig-Five personalitydyadic interactionMARS datasetgenerative modelschallenge benchmark
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The pith

REACT 2026 extends the MARS dataset with Big-Five personality labels and EEG recordings to support one-to-many personalized facial reaction generation.

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

The paper establishes a new challenge setting in which machine learning models must generate multiple appropriate facial reactions that are personalized to a specific listener by incorporating that listener's personality traits and brain activity signals. It builds directly on prior REACT challenges by augmenting the existing MARS dataset with individual-level Big-Five personality labels and EEG recordings. A sympathetic reader would care because current dyadic interaction models largely ignore the combination of behavioral, affective, and neurophysiological signals when producing listener responses. The challenge defines four sub-tasks covering offline and online versions of both generic and personalized multiple appropriate facial reaction generation, and supplies new baselines for each.

Core claim

The central claim is that providing the MARS dataset together with Big-Five personality labels and EEG recordings creates a new one-to-many personalised facial reaction generation setting that combines human expressive behavioural, affective and neurophysiological signals and remains largely unexplored in current dyadic interaction modelling.

What carries the argument

The MARS dataset augmented with individual-level Big-Five personality labels and EEG recordings, which supplies the input signals for the one-to-many personalised MAFRG task.

If this is right

  • Models can now be developed and benchmarked for offline generic MAFRG.
  • Models can now be developed and benchmarked for offline personalised MAFRG.
  • Models can now be developed and benchmarked for online generic MAFRG.
  • Models can now be developed and benchmarked for online personalised MAFRG.

Where Pith is reading between the lines

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

  • The same augmented signals could be tested for predicting listener EEG responses rather than only facial reactions.
  • Personality and EEG features might be used to adapt reaction generation in real time during live conversations.
  • The setting could be extended to other modalities such as voice or gesture to check whether the personalization benefit generalizes.

Load-bearing premise

That adding Big-Five personality labels and EEG recordings to the MARS dataset will enable models to produce personalised, appropriate, diverse, realistic and synchronised reactions.

What would settle it

A controlled experiment in which models trained with the added personality and EEG data show no measurable improvement in personalization or appropriateness metrics compared with models trained on the original MARS data alone.

Figures

Figures reproduced from arXiv: 2606.07935 by Andrew Howes, Cheng Luo, Cristina Palmero, Elisabeth Andre, Fabien Ringeval, German Barquero, Hatice Gunes, Michel Valstar, Micol Spitale, Mohamed Daoudi, Sergio Escalera, Siyang Song, Xiangyu Kong, Zijian Wu.

Figure 1
Figure 1. Figure 1: Illustration of the data collection of the MARS [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Pipelines for four target MAFRG tasks. Offline generic MAFRG models directly takes each entire speaker audio-visual [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

In dyadic interactions, various human facial reactions could be appropriate for responding to each human speaker behaviour. Following the successful organisation of the REACT 2023, 2024 and 2025 challenge series, a body of generative deep learning (DL) models have been developed for the problem of multiple appropriate facial reaction generation (MAFRG). This year, we propose the REACT 2026 challenge encouraging the development and benchmarking of Machine Learning (ML) models that can generate multiple personalised, appropriate, diverse, realistic and synchronised human-style facial reactions expressed by a specific human listener for responding to each given speaker behaviour. As a key of the challenge, we continuously provide challenge participants with MARS dataset introduced by REACT 2025 but additionally provide individual-level Big-Five personality labels and EEG recordings. This introduces a new one-to-many personalised facial reaction generation setting combining human expressive behavioural, affective and neurophysiological signals, which remains largely unexplored in current dyadic interaction modelling. This paper also presents the challenge guidelines and new baselines on the four proposed sub-challenges: Offline generic and personalised MAFRG as well as Online generic and personalised MAFRG, respectively, which are publicly available at https://github.com/reactmultimodalchallenge/baseline_react2026.

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

0 major / 0 minor

Summary. The manuscript announces the REACT 2026 challenge, the fourth in the series, which defines four sub-challenges (offline/online generic MAFRG and offline/online personalised MAFRG). It augments the prior MARS dataset with individual-level Big-Five personality labels and EEG recordings to support development of models generating multiple personalised, appropriate, diverse, realistic and synchronised facial reactions, supplies challenge guidelines, and releases baseline implementations on GitHub.

Significance. The public release of baselines and the multimodal extension of an existing dataset provide a concrete, reproducible starting point for exploring neurophysiological and personality-conditioned reaction generation, an area noted as largely unexplored. If the challenge attracts competitive submissions, it could help establish standardised evaluation protocols for one-to-many personalised dyadic modelling.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the REACT 2026 challenge manuscript, the recognition of its significance in providing reproducible baselines and multimodal extensions, and the recommendation to accept.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is a challenge organization document announcing four sub-challenges on the augmented MARS dataset. It contains no mathematical derivations, equations, fitted parameters, predictions, or uniqueness theorems. The text only defines tasks, supplies baselines, and states that the combined behavioural/affective/neurophysiological setting is provided for participants to explore; no claim reduces to a self-definition or self-citation chain. This matches the default non-circular case for dataset/challenge papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities because the document is a challenge description without theoretical derivations or new postulated constructs.

pith-pipeline@v0.9.1-grok · 5813 in / 1057 out tokens · 25554 ms · 2026-06-27T20:25:51.585949+00:00 · methodology

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