EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation

• URL: http://arxiv.org/abs/2402.01422v1
• Date: Fri, 2 Feb 2024 14:04:18 GMT
• Title: EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation
• Authors: Guanwen Feng, Haoran Cheng, Yunan Li, Zhiyuan Ma, Chaoneng Li, Zhihao Qian, Qiguang Miao, Chi-Man Pun
• Abstract summary: We propose a visual attribute-guided audio decoupler to generate fine-grained facial animations. To achieve more precise emotional expression, we introduce a fine-grained emotion coefficient prediction module. Our proposed method, EmoSpeaker, outperforms existing emotional talking face generation methods in terms of expression variation and lip synchronization.
• Score: 34.5592743467339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Implementing fine-grained emotion control is crucial for emotion generation tasks because it enhances the expressive capability of the generative model, allowing it to accurately and comprehensively capture and express various nuanced emotional states, thereby improving the emotional quality and personalization of generated content. Generating fine-grained facial animations that accurately portray emotional expressions using only a portrait and an audio recording presents a challenge. In order to address this challenge, we propose a visual attribute-guided audio decoupler. This enables the obtention of content vectors solely related to the audio content, enhancing the stability of subsequent lip movement coefficient predictions. To achieve more precise emotional expression, we introduce a fine-grained emotion coefficient prediction module. Additionally, we propose an emotion intensity control method using a fine-grained emotion matrix. Through these, effective control over emotional expression in the generated videos and finer classification of emotion intensity are accomplished. Subsequently, a series of 3DMM coefficient generation networks are designed to predict 3D coefficients, followed by the utilization of a rendering network to generate the final video. Our experimental results demonstrate that our proposed method, EmoSpeaker, outperforms existing emotional talking face generation methods in terms of expression variation and lip synchronization. Project page: https://peterfanfan.github.io/EmoSpeaker/

Related papers

• Audio-Driven Emotional 3D Talking-Head Generation [47.6666060652434]
  We present a novel system for synthesizing high-fidelity, audio-driven video portraits with accurate emotional expressions. We propose a pose sampling method that generates natural idle-state (non-speaking) videos in response to silent audio inputs.
  arXiv  Detail & Related papers  (2024-10-07T08:23:05Z)
• 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)
• DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation [14.07086606183356]
  Speech-driven 3D facial animation has garnered lots of attention thanks to its broad range of applications. Current methods fail to capture the nuanced emotional undertones conveyed through speech and produce monotonous facial motion. We introduce DEEPTalk, a novel approach that generates diverse and emotionally rich 3D facial expressions directly from speech inputs.
  arXiv  Detail & Related papers  (2024-08-12T08:56:49Z)
• EmoFace: Audio-driven Emotional 3D Face Animation [3.573880705052592]
  EmoFace is a novel audio-driven methodology for creating facial animations with vivid emotional dynamics. Our approach can generate facial expressions with multiple emotions, and has the ability to generate random yet natural blinks and eye movements. Our proposed methodology can be applied in producing dialogues animations of non-playable characters in video games, and driving avatars in virtual reality environments.
  arXiv  Detail & Related papers  (2024-07-17T11:32:16Z)
• DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation [75.90730434449874]
  We introduce DREAM-Talk, a two-stage diffusion-based audio-driven framework, tailored for generating diverse expressions and accurate lip-sync concurrently. Given the strong correlation between lip motion and audio, we then refine the dynamics with enhanced lip-sync accuracy using audio features and emotion style. Both quantitatively and qualitatively, DREAM-Talk outperforms state-of-the-art methods in terms of expressiveness, lip-sync accuracy and perceptual quality.
  arXiv  Detail & Related papers  (2023-12-21T05:03:18Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• 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)
• Emotionally Enhanced Talking Face Generation [52.07451348895041]
  We build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions. We show that our model can adapt to arbitrary identities, emotions, and languages. Our proposed framework is equipped with a user-friendly web interface with a real-time experience for talking face generation with emotions.
  arXiv  Detail & Related papers  (2023-03-21T02:33:27Z)
• EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation [28.964917860664492]
  Speech-driven 3D face animation aims to generate realistic facial expressions that match the speech content and emotion. This paper proposes an end-to-end neural network to disentangle different emotions in speech so as to generate rich 3D facial expressions. Our approach outperforms state-of-the-art methods and exhibits more diverse facial movements.
  arXiv  Detail & Related papers  (2023-03-20T13:22:04Z)
• Emotion Intensity and its Control for Emotional Voice Conversion [77.05097999561298]
  Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding.
  arXiv  Detail & Related papers  (2022-01-10T02:11:25Z)

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.