Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents

• URL: http://arxiv.org/abs/2606.16481v1
• Date: Mon, 15 Jun 2026 09:49:23 GMT
• Title: Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents
• Abstract summary: Art therapy plays a vital role in emotional healing, in which narrative creation acts as the primary vehicle for emotional expression.<n>With the rapid development of Large Language Models (LLMs), automated narrative generation technology has provided a new pathway to support such artistic designs.<n>This paper proposes EC-Script, an agent-based framework that enables hierarchical control of the affective trajectory in narrative generation for emotional healing.
• Score: 15.997100974367195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Art therapy plays a vital role in emotional healing, in which narrative creation acts as the primary vehicle for emotional expression. Given the inherently dynamic nature of emotions during healing, narratives with finely controlled emotional fluctuations enable individuals to safely project inner conflicts and achieve emotional catharsis. Recently, with the rapid development of Large Language Models (LLMs), automated narrative generation technology has provided a new pathway to support such artistic designs. However, while existing methods can produce fluent texts, they struggle to generate narratives that adhere to specified affective trajectories, failing to meet the demands of emotion-oriented psychological healing. To address these issues, this paper proposes EC-Script, an LLM agent-based framework that enables hierarchical control of the affective trajectory in narrative generation for emotional healing. To ensure that the generated narratives strictly follow the given emotional patterns, EC-Script establishes overall narrative direction through Emotion-Trajectory Planning, propels scene-level plot development with Character-Driven Scene Generation, and regulates local emotional changes of characters via Emotion-Controlled Script Writing. Ultimately, it outputs scene-by-scene script content that remains highly consistent with the preset affective trajectory. Experimental results demonstrate that EC-Script significantly outperforms baseline methods in affective trajectory adherence, exhibiting excellent and reliable emotional controllability, thereby providing effective technical support for AI-assisted emotional healing scenarios.

Related papers

• EmoStory: Emotion-Aware Story Generation [8.859038452064818]
  We introduce emotion-aware story generation, a new task that aims to generate subject-consistent visual stories with explicit emotional directions.<n>EmoStory is a two-stage framework that integrates agent-based story planning and region-aware story generation.<n>We evaluate EmoStory on a newly constructed dataset covering 25 subjects and 600 emotional stories.
  arXiv  Detail & Related papers  (2026-03-11T02:44:25Z)
• Text-Driven Emotionally Continuous Talking Face Generation [24.062908127306695]
  We propose Emotionally Continuous Talking Face Generation (EC-TFG) to synthesize realistic videos.<n>EC-TFG takes a text segment and an emotion description with varying emotions as driving data, aiming to generate a video where the person speaks the text.<n>We introduce a customized model, i.e., Temporal-Intensive Emotion Modulated Talking Face Generation (TIE-TFG), which innovatively manages dynamic emotional variations.
  arXiv  Detail & Related papers  (2026-03-06T09:24:43Z)
• A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
  This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-08T14:07:30Z)
• EmoCAST: Emotional Talking Portrait via Emotive Text Description [56.42674612728354]
  EmoCAST is a diffusion-based framework for precise text-driven emotional synthesis.<n>In appearance modeling, emotional prompts are integrated through a text-guided decoupled emotive module.<n>EmoCAST achieves state-of-the-art performance in generating realistic, emotionally expressive, and audio-synchronized talking-head videos.
  arXiv  Detail & Related papers  (2025-08-28T10:02:06Z)
• Taming Transformer for Emotion-Controllable Talking Face Generation [61.835295250047196]
  We propose a novel method to tackle the emotion-controllable talking face generation task discretely.<n>Specifically, we employ two pre-training strategies to disentangle audio into independent components and quantize videos into combinations of visual tokens.<n>We conduct experiments on the MEAD dataset that controls the emotion of videos conditioned on multiple emotional audios.
  arXiv  Detail & Related papers  (2025-08-20T02:16:52Z)
• Prompt-Unseen-Emotion: Zero-shot Expressive Speech Synthesis with Prompt-LLM Contextual Knowledge for Mixed Emotions [38.122477830163255]
  This paper proposes a novel prompt-unseen-emotion (PUE) approach to generate unseen emotional speech via emotion-guided prompt learning.<n>Our proposed PUE successfully facilitates expressive speech synthesis of unseen emotions in a zero-shot setting.
  arXiv  Detail & Related papers  (2025-06-03T10:59:22Z)
• UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech [61.989360995528905]
  We propose UDDETTS, a universal framework unifying discrete and dimensional emotions for controllable emotional TTS.<n>This model introduces the interpretable Arousal-Dominance-Valence (ADV) space for dimensional emotion description and supports emotion control driven by either discrete emotion labels or nonlinearly quantified ADV values.<n>Experiments show that UDDETTS achieves linear emotion control along three interpretable dimensions, and exhibits superior end-to-end emotional speech synthesis capabilities.
  arXiv  Detail & Related papers  (2025-05-15T12:57:19Z)
• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics.<n>We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention.<n>Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks.<n>Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• Dual-path Collaborative Generation Network for Emotional Video Captioning [33.230028098522254]
  Emotional Video Captioning is an emerging task that aims to describe factual content with the intrinsic emotions expressed in videos. Existing emotional video captioning methods perceive global visual emotional cues at first, and then combine them with the video features to guide the emotional caption generation. We propose a dual-path collaborative generation network, which dynamically perceives visual emotional cues evolutions while generating emotional captions.
  arXiv  Detail & Related papers  (2024-08-06T07:30:53Z)
• ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains [61.50113532215864]
  Causal Emotion Entailment (CEE) aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. Current works in CEE mainly focus on modeling semantic and emotional interactions in conversations. We introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations.
  arXiv  Detail & Related papers  (2024-05-17T15:45:08Z)
• Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought [50.13429055093534]
  Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks. We propose the Emotional Chain-of-Thought (ECoT) to enhance the performance of LLMs on various emotional generation tasks.
  arXiv  Detail & Related papers  (2024-01-12T16:42:10Z)

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.