Self-Diffuser:语音驱动人脸表情的技术研究

Self-Diffuser: Research on the Technology of Speech-Driven Facial Expressions

先前的语音驱动面部表情的动画研究从音频信号中产生了较为逼真和精确的嘴唇运动和面部表情。传统的方法主要集中在学习从语音到动画的确定性映射,最近的研究开始探讨语音驱动的3D人脸动画的多样性,即通过利用扩散模型的多样性能力来捕捉音频和面部运动之间复杂的多对多关系来完成任务。本文的Self-Diffuser方法使用预训练的大语言模型wav2vec 2.0对音频输入进行编码,通过引入基于扩散的技术,将其与Transformer相结合来完成生成任务。本研究不仅克服了传统回归模型在生成具有唇读可理解性的真实准确唇运动方面的局限性,还探讨了精确的嘴唇同步和创造与语音无关的面部表情之间的权衡。通过对比、分析当前最先进的方法,本文的Self-Diffuser方法,使得语音驱动的面部动画产生了更精确的唇运动;在与说话松散相关的上半部表情方面也产生了更贴近于真实说话表情的面部运动;同时本文模型引入的扩散机制使得生成3D人脸动画序列的多样性能力也大大提高。Previous research on speech-driven facial expression animation has achieved realistic and accurate lip movements and facial expressions from audio signals. Traditional methods primarily focused on learning deterministic mappings from speech to animation. Recent studies have started exploring the diversity of speech-driven 3D facial animation, aiming to capture the complex many-to-many relationships between audio and facial motion by leveraging the diversity capabilities of diffusion models. In this study, the Self-Diffuser method is proposed by utilizing the pre-trained large-scale language model wav2vec 2.0 to encode audio inputs. By introducing diffusion-based techniques and combining them with Transformers, the generation task is accomplished. This research not only overcomes the limitations of traditional regression models in generating lip movements that are both realistic and lip-reading comprehensible, but also explores the trade-off between precise lip synchronization and creating facial expressions independent of speech. Through comparisons and analysis with the current state-of-the-art methods, the Self-Diffuser method in this paper achieves more accurate lip movements in speech-driven facial animation. It also produces facial motions that closely resemble real speaking expressions in the upper face region correlated with speech looseness. Additionally, the introduced diffusion mechanism significantly enhances the diversity capabilities in generating 3D facial animation sequences.