Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video

• URL: http://arxiv.org/abs/2604.07786v1
• Date: Thu, 09 Apr 2026 04:28:03 GMT
• Title: Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video
• Abstract summary: Cross-Modal Emotion Transfer (C-MET) is a novel approach that generates facial expressions based on speeches.<n>Our method improves emotion accuracy by 14% over state-of-the-art methods.
• Score: 17.472242712450473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthesized videos, emotion editing in talking face video plays a crucial role. However, existing approaches often limit expressive flexibility and struggle to generate extended emotions. Label-based methods represent emotions with discrete categories, which fail to capture a wide range of emotions. Audio-based methods can leverage emotionally rich speech signals - and even benefit from expressive text-to-speech (TTS) synthesis - but they fail to express the target emotions because emotions and linguistic contents are entangled in emotional speeches. Images-based methods, on the other hand, rely on target reference images to guide emotion transfer, yet they require high-quality frontal views and face challenges in acquiring reference data for extended emotions (e.g., sarcasm). To address these limitations, we propose Cross-Modal Emotion Transfer (C-MET), a novel approach that generates facial expressions based on speeches by modeling emotion semantic vectors between speech and visual feature spaces. C-MET leverages a large-scale pretrained audio encoder and a disentangled facial expression encoder to learn emotion semantic vectors that represent the difference between two different emotional embeddings across modalities. Extensive experiments on the MEAD and CREMA-D datasets demonstrate that our method improves emotion accuracy by 14% over state-of-the-art methods, while generating expressive talking face videos - even for unseen extended emotions. Code, checkpoint, and demo are available at https://chanhyeok-choi.github.io/C-MET/

Related papers

• Adaptive Emotional Video Captioning via Affective Heterogeneous Graph Reasoning and Multi-task Joint Learning [26.623474533402575]
  We propose SAGML, an adaptive EVC framework via affective heterogeneous graph and multi-task language modeling.<n> SAGML constructs a soft affective heterogeneous graph containing catalog-level emotion nodes and lexical-level emotion word nodes.<n>It provides an error-resilient and multi-emotion-aware baseline for EVC.
  arXiv  Detail & Related papers  (2026-07-31T05:49:33Z)
• 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)
• 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)
• 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)
• EmoDiffusion: Enhancing Emotional 3D Facial Animation with Latent Diffusion Models [66.67979602235015]
  EmoDiffusion is a novel approach that disentangles different emotions in speech to generate rich 3D emotional facial expressions.<n>We capture facial expressions under the guidance of animation experts using LiveLinkFace on an iPhone.
  arXiv  Detail & Related papers  (2025-03-14T02:54:22Z)
• EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion [5.954758598327494]
  EMOdiffhead is a novel method for emotional talking head video generation. It enables fine-grained control of emotion categories and intensities. It achieves state-of-the-art performance compared to other emotion portrait animation methods.
  arXiv  Detail & Related papers  (2024-09-11T13:23:22Z)
• Emotional Speech-Driven Animation with Content-Emotion Disentanglement [51.34635009347183]
  We propose EMOTE, which generates 3D talking-head avatars that maintain lip-sync from speech while enabling explicit control over the expression of emotion. EmOTE produces speech-driven facial animations with better lip-sync than state-of-the-art methods trained on the same data.
  arXiv  Detail & Related papers  (2023-06-15T09:31:31Z)
• High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning [43.09015109281053]
  We propose a more flexible and generalized framework for talking face generation. Specifically, we supplement the emotion style in text prompts and use an Aligned Multi-modal Emotion encoder to embed the text, image, and audio emotion modality into a unified space. An Emotion-aware Audio-to-3DMM Convertor is proposed to connect the emotion condition and the audio sequence to structural representation.
  arXiv  Detail & Related papers  (2023-05-04T05:59:34Z)

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.