AffectVerse: Emotional World Models for Multimodal Affective Computing

• URL: http://arxiv.org/abs/2605.19950v1
• Date: Tue, 19 May 2026 15:05:00 GMT
• Title: AffectVerse: Emotional World Models for Multimodal Affective Computing
• Abstract summary: We propose AffectVerse, an action-free representation-level module for short-horizon latent affective prediction.<n>EWM contains three modules: 1) Cross-Modal Temporal Imagination predicts future video/audio representations from past tokens with multi-step rollout.<n>EWM compresses imagined tokens into modality-aware belief tokens.<n>These results suggest predictive belief-state modeling is a practical alternative for affective computing.
• Score: 56.144242722718985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans infer emotions by integrating observed multimodal cues with expectations about how affective states may unfold. Existing multimodal large language models (MLLMs), however, often treat emotion recognition as static fusion over complete audiovisual-text inputs, leaving affective dynamics implicit. We propose AffectVerse, a Qwen2.5-Omni-based model equipped with an Emotion World Module (EWM), an action-free representation-level module for short-horizon latent affective prediction. \rev{EWM contains three modules: 1) Cross-Modal Temporal Imagination predicts future video/audio representations from past tokens with multi-step rollout. 2) MAMA(Modality-Aware Multi-step Attention) Belief Aggregation compresses imagined tokens into modality-aware belief tokens. 3) Belief Injection inserts these belief tokens into the LLM for affective reasoning.} AffectVerse uses future prediction as a past-conditioned self-supervised signal: it does not replace modeling observed history or require unseen signals at inference, but forces the current belief state to encode transition cues that are predictive of subsequent affective change. Across nine benchmarks, AffectVerse improves at least 2.57\% over other models, while controlled ablations show additive gains from temporal imagination, cross-modal rollout, and belief aggregation. These results suggest predictive belief-state modeling is a practical alternative for affective computing.

Related papers

• UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling [73.35395707747456]
  Recent advances in Vision-Language-Action models have improved robotic manipulation, yet observation-to-action learning remains limited by a fundamental transition realizability gap.<n>A visually plausible predicted future observation does not necessarily correspond to a physically realizable transition.<n>A historically executable action pattern may not necessarily realize the intended transition in the current scene and therefore requires context-aware adaptation.
  arXiv  Detail & Related papers  (2026-09-10T17:45:03Z)
• Ego-Human Motion Prediction with 3D-Aware LLM [51.5064259568401]
  Ego3DLM is a tool for predicting human motion from an egocentric perspective.<n>It decodes past pose, future pose, past narration, and future narration in a single autoregressive pass.<n>Ego3DLM achieves state-of-the-art performance across future motion prediction, past motion tracking, and motion description.
  arXiv  Detail & Related papers  (2026-07-08T04:51:24Z)
• SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations [9.535514750203266]
  We introduce Speaker-Conditioned Priors over Emotions (SCoPE)<n>SCoPE is a light weight module that utilizes the emotional history of each speaker.<n>We incorporate emotion shift prediction to guide the model in balancing the priors from SCoPE and multimodal evidence.
  arXiv  Detail & Related papers  (2026-05-13T13:00:38Z)
• Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model [0.0]
  Short-term human pose prediction plays a crucial role in interactive systems, assistive robots, and emotion-aware human-computer interaction.<n>This paper investigates whether facial expression-derived emotion embeddings can provide auxiliary conditional signals for short-term pose prediction.
  arXiv  Detail & Related papers  (2026-04-26T04:56:45Z)
• SELF-EMO: Emotional Self-Evolution from Recognition to Consistent Expression [13.896538941019722]
  We propose SELF-EMO, a self-evolution framework grounded in the hypothesis that better emotion prediction leads to more consistent emotional responses.<n>We introduce two auxiliary tasks, emotional understanding and emotional expression, and design a role-based self-play paradigm where the model acts as both an emotion recognizer and a dialogue responder.<n> Experiments on IEMOCAP, MELD, and EmoryNLP show that SELF-EMO achieves state-of-the-art performance, improving accuracy by +6.33% on Qwen3-4B and +8.54% on Qwen3-8B.
  arXiv  Detail & Related papers  (2026-04-20T09:27:40Z)
• Multimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling [2.8037951156321377]
  We propose a multimodal emotion recognition framework for the Expression task in the 10th Affective Behavior Analysis in-the-wild (ABAW) Challenge.<n>Our framework builds on large-scale pre-trained models for visual and audio representation learning and integrates them in a unified multimodal architecture.<n> Experimental results on the ABAW 10th EXPR benchmark demonstrate the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2026-03-12T14:20:29Z)
• Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition [56.00118641432005]
  We propose a Memory-guided Prototypical Co-occurrence Learning framework that explicitly models emotion co-occurrence patterns.<n>Inspired by human cognitive memory systems, we introduce a memory retrieval strategy to extract semantic-level co-occurrence associations.<n>Our model learns affectively informative representations for accurate emotion distribution prediction.
  arXiv  Detail & Related papers  (2026-02-24T04:11:25Z)
• Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding [45.13650362585136]
  We present Emotion-LLaMAv2 and the MMEVerse benchmark, establishing an end-to-end pipeline together with a standardized evaluation setting for emotion recognition and reasoning.<n>An end-to-end multiview encoder eliminates external face detection and captures nuanced emotional cues via richer spatial and temporal multiview tokens.<n>A perception-to-cognition curriculum instruction tuning scheme within the LLaMA2 backbone unifies emotion recognition and free-form emotion reasoning.
  arXiv  Detail & Related papers  (2026-01-23T05:02:43Z)
• Ego-centric Predictive Model Conditioned on Hand Trajectories [52.531681772560724]
  In egocentric scenarios, anticipating both the next action and its visual outcome is essential for understanding human-object interactions.<n>We propose a unified two-stage predictive framework that jointly models action and visual future in egocentric scenarios.<n>Our approach is the first unified model designed to handle both egocentric human activity understanding and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-08-27T13:09:55Z)
• TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Prediction [1.8923948104852863]
  We propose TransFusion, an innovative and practical diffusion-based model for 3D human motion prediction. Our model leverages Transformer as the backbone with long skip connections between shallow and deep layers. In contrast to prior diffusion-based models that utilize extra modules like cross-attention and adaptive layer normalization, we treat all inputs, including conditions, as tokens to create a more lightweight model.
  arXiv  Detail & Related papers  (2023-07-30T01:52:07Z)
• MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition [118.73025093045652]
  We propose a pre-training model textbfMEmoBERT for multimodal emotion recognition. Unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text prediction. Our proposed MEmoBERT significantly enhances emotion recognition performance.
  arXiv  Detail & Related papers  (2021-10-27T09:57:00Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)
• Knowledge Distillation for Action Anticipation via Label Smoothing [21.457069042129138]
  Human capability to anticipate near future from visual observations and non-verbal cues is essential for developing intelligent systems. We implement a multi-modal framework based on long short-term memory (LSTM) networks to summarize past observations and make predictions at different time steps. Experiments show that label smoothing systematically improves performance of state-of-the-art models for action anticipation.
  arXiv  Detail & Related papers  (2020-04-16T15:38:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.