SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation
Changpeng Cai, Guinan Guo, Jiao Li, Junhao Su, Fei Shen, Chenghao He,, Jing Xiao, Yuanxu Chen, Lei Dai, Feiyu Zhu

TL;DR
SPEAK is a novel one-shot talking head generation framework that enables independent control of lip synchronization, facial emotions, and head poses, producing realistic and expressive facial animations from speech.
Contribution
The paper introduces IRFD for feature disentanglement and a face editing module for emotional and pose control, advancing one-shot talking head generation capabilities.
Findings
Ensures lip synchronization with speech.
Enables decoupled control of facial features.
Produces realistic, expressive facial animations.
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…
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Taxonomy
TopicsSpeech and dialogue systems · Social Robot Interaction and HRI · Phonetics and Phonology Research
