Facial Expression Generation Aligned with Human Preference for Natural Dyadic Interaction

• URL: http://arxiv.org/abs/2603.07093v1
• Date: Sat, 07 Mar 2026 08:03:21 GMT
• Title: Facial Expression Generation Aligned with Human Preference for Natural Dyadic Interaction
• Abstract summary: We propose a facial expression generation method aligned with human preference.<n>A key to our method is framing the generation of identity-independent facial expressions as an action learning process.<n> Experiments on two benchmarks demonstrate that our method effectively aligns facial expressions with human preference.
• Score: 53.12445980832305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving natural dyadic interaction requires generating facial expressions that are emotionally appropriate and socially aligned with human preference. Human feedback offers a compelling mechanism to guide such alignment, yet how to effectively incorporate this feedback into facial expression generation remains underexplored. In this paper, we propose a facial expression generation method aligned with human preference by leveraging human feedback to produce contextually and emotionally appropriate expressions for natural dyadic interaction. A key to our method is framing the generation of identity-independent facial expressions as an action learning process, allowing human feedback to assess their validity free from visual or identity bias. We establish a closed feedback loop in which listener expressions dynamically respond to evolving conversational cues of the speaker. Concretely, we train a vision-language-action model via supervised fine-tuning to map the speaker's multimodal signals into controllable low-dimensional expression representations of a 3D morphable model. We further introduce a human-feedback reinforcement learning strategy that integrates the imitation of high-quality expression response with critic-guided optimization. Experiments on two benchmarks demonstrate that our method effectively aligns facial expressions with human preference and achieves superior performance.

Related papers

• Let Me Look at You: Advanced Facial Expression Modeling for Conversational Speech Synthesis [36.79172273160707]
  Facial expressions encode subtle and rich affective cues that are crucial for empathetic speech interaction.<n>We propose FacialTalker, a facial-expression-aware CSS framework built upon a large language model backbone.<n>We construct VSDD-1K, a large-scale multimodal dialogue dataset collected through a fully automated pipeline from real-world Internet conversations.
  arXiv  Detail & Related papers  (2026-07-27T13:42:37Z)
• 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)
• Think-Before-Draw: Decomposing Emotion Semantics & Fine-Grained Controllable Expressive Talking Head Generation [7.362433184546492]
  Emotional talking-head generation has emerged as a pivotal research area at the intersection of computer vision and multimodal artificial intelligence.<n>This study proposes the Think-Before-Draw framework to address two key challenges.
  arXiv  Detail & Related papers  (2025-07-17T03:33:46Z)
• HAPI: A Model for Learning Robot Facial Expressions from Human Preferences [4.250785086838609]
  We propose a novel learning-to-rank framework that leverages human feedback to address this discrepancy.<n>Our approach produces significantly more realistic and socially resonant expressions of Anger, Happiness, and Surprise than those generated by baseline and expert-designed methods.
  arXiv  Detail & Related papers  (2025-03-21T11:04:01Z)
• When Words Smile: Generating Diverse Emotional Facial Expressions from Text [77.1867389815291]
  We introduce an end-to-end text-to-expression model that explicitly focuses on emotional dynamics.<n>Our model learns expressive facial variations in a continuous latent space and generates expressions that are diverse, fluid, and emotionally coherent.
  arXiv  Detail & Related papers  (2024-12-03T15:39:05Z)
• How Do You Perceive My Face? Recognizing Facial Expressions in Multi-Modal Context by Modeling Mental Representations [5.895694050664867]
  We introduce a novel approach for facial expression classification that goes beyond simple classification tasks. Our model accurately classifies a perceived face and synthesizes the corresponding mental representation perceived by a human when observing a face in context. We evaluate synthesized expressions in a human study, showing that our model effectively produces approximations of human mental representations.
  arXiv  Detail & Related papers  (2024-09-04T09:32:40Z)
• Towards Localized Fine-Grained Control for Facial Expression Generation [54.82883891478555]
  Humans, particularly their faces, are central to content generation due to their ability to convey rich expressions and intent. Current generative models mostly generate flat neutral expressions and characterless smiles without authenticity. We propose the use of AUs (action units) for facial expression control in face generation.
  arXiv  Detail & Related papers  (2024-07-25T18:29:48Z)
• Dyadic Interaction Modeling for Social Behavior Generation [6.626277726145613]
  We present an effective framework for creating 3D facial motions in dyadic interactions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach. Experiments demonstrate the superiority of our framework in generating listener motions.
  arXiv  Detail & Related papers  (2024-03-14T03:21:33Z)
• From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations [107.88375243135579]
  Given speech audio, we output multiple possibilities of gestural motion for an individual, including face, body, and hands. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures. Experiments show our model generates appropriate and diverse gestures, outperforming both diffusion- and VQ-only methods.
  arXiv  Detail & Related papers  (2024-01-03T18:55:16Z)
• Emotional Listener Portrait: Realistic Listener Motion Simulation in Conversation [50.35367785674921]
  Listener head generation centers on generating non-verbal behaviors of a listener in reference to the information delivered by a speaker. A significant challenge when generating such responses is the non-deterministic nature of fine-grained facial expressions during a conversation. We propose the Emotional Listener Portrait (ELP), which treats each fine-grained facial motion as a composition of several discrete motion-codewords. Our ELP model can not only automatically generate natural and diverse responses toward a given speaker via sampling from the learned distribution but also generate controllable responses with a predetermined attitude.
  arXiv  Detail & Related papers  (2023-09-29T18:18:32Z)
• Comprehensive Facial Expression Synthesis using Human-Interpretable Language [33.11402372756348]
  We propose a new facial expression synthesis model from language-based facial expression description. Our method can synthesize the facial image with detailed expressions. In addition, effectively embedding language features on facial features, our method can control individual word to handle each part of facial movement.
  arXiv  Detail & Related papers  (2020-07-16T07:28:25Z)

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.