SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation

• URL: http://arxiv.org/abs/2405.07257v3
• Date: Mon, 04 Nov 2024 16:42:38 GMT
• Title: SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation
• Authors: Changpeng Cai, Guinan Guo, Jiao Li, Junhao Su, Fei Shen, Chenghao He, Jing Xiao, Yuanxu Chen, Lei Dai, Feiyu Zhu,
• Abstract summary: We propose a novel one-shot Talking Head Generation framework (SPEAK) that distinguishes itself from the general Talking Face Generation. We introduce Inter-Reconstructed Feature Disentanglement (IRFD) module to decouple facial features into three latent spaces. We then design a face editing module that modifies speech content and facial latent codes into a single latent space.
• Score: 13.459396544300137
• License:
• Abstract: Most earlier researches on talking face generation have focused on the synchronization of lip motion and speech content. However, head pose and facial emotions are equally important characteristics of natural faces. While audio-driven talking face generation has seen notable advancements, existing methods either overlook facial emotions or are limited to specific individuals and cannot be applied to arbitrary subjects. In this paper, we propose a novel one-shot Talking Head Generation framework (SPEAK) that distinguishes itself from the general Talking Face Generation by enabling emotional and postural control. Specifically, we introduce Inter-Reconstructed Feature Disentanglement (IRFD) module to decouple facial features into three latent spaces. Then we design a face editing module that modifies speech content and facial latent codes into a single latent space. Subsequently, we present a novel generator that employs modified latent codes derived from the editing module to regulate emotional expression, head poses, and speech content in synthesizing facial animations. Extensive trials demonstrate that our method ensures lip synchronization with the audio while enabling decoupled control of facial features, it can generate realistic talking head with coordinated lip motions, authentic facial emotions, and smooth head movements. The demo video is available: https://anonymous.4open.science/r/SPEAK-8A22

Related papers

• 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)
• Emotional Conversation: Empowering Talking Faces with Cohesive Expression, Gaze and Pose Generation [12.044308738509402]
  We propose a two-stage audio-driven talking face generation framework that employs 3D facial landmarks as intermediate variables. This framework achieves collaborative alignment of expression, gaze, and pose with emotions through self-supervised learning. Our model significantly advances the state-of-the-art performance in both visual quality and emotional alignment.
  arXiv  Detail & Related papers  (2024-06-12T06:00:00Z)
• 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)
• AdaMesh: Personalized Facial Expressions and Head Poses for Adaptive Speech-Driven 3D Facial Animation [49.4220768835379]
  AdaMesh is a novel adaptive speech-driven facial animation approach. It learns the personalized talking style from a reference video of about 10 seconds. It generates vivid facial expressions and head poses.
  arXiv  Detail & Related papers  (2023-10-11T06:56:08Z)
• Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video [91.92782707888618]
  We present a decomposition-composition framework named Speech to Lip (Speech2Lip) that disentangles speech-sensitive and speech-insensitive motion/appearance. We show that our model can be trained by a video of just a few minutes in length and achieve state-of-the-art performance in both visual quality and speech-visual synchronization.
  arXiv  Detail & Related papers  (2023-09-09T14:52:39Z)
• 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)
• Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations [61.65012981435094]
  DIRFA is a novel method that can generate talking faces with diverse yet realistic facial animations from the same driving audio. To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network. We show that DIRFA can generate talking faces with realistic facial animations effectively.
  arXiv  Detail & Related papers  (2023-04-18T12:36:15Z)
• That's What I Said: Fully-Controllable Talking Face Generation [16.570649208028343]
  We propose a canonical space where every face has the same motion patterns but different identities. The second is to navigate a multimodal motion space that only represents motion-related features while eliminating identity information. Our method can generate natural-looking talking faces with fully controllable facial attributes and accurate lip synchronisation.
  arXiv  Detail & Related papers  (2023-04-06T17:56:50Z)
• Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation [96.66010515343106]
  We propose a clean yet effective framework to generate pose-controllable talking faces. We operate on raw face images, using only a single photo as an identity reference. Our model has multiple advanced capabilities including extreme view robustness and talking face frontalization.
  arXiv  Detail & Related papers  (2021-04-22T15:10:26Z)

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.