LinguaLinker: Audio-Driven Portraits Animation with Implicit Facial Control Enhancement
This addresses the problem of creating realistic, language-agnostic animated portraits for applications like entertainment or communication, representing an incremental advance over traditional methods.
The study tackled synchronizing facial animations with multilingual audio using a diffusion-based framework, achieving significant improvements in fidelity, lip-syncing accuracy, and motion variations for any portrait and language.
This study delves into the intricacies of synchronizing facial dynamics with multilingual audio inputs, focusing on the creation of visually compelling, time-synchronized animations through diffusion-based techniques. Diverging from traditional parametric models for facial animation, our approach, termed LinguaLinker, adopts a holistic diffusion-based framework that integrates audio-driven visual synthesis to enhance the synergy between auditory stimuli and visual responses. We process audio features separately and derive the corresponding control gates, which implicitly govern the movements in the mouth, eyes, and head, irrespective of the portrait's origin. The advanced audio-driven visual synthesis mechanism provides nuanced control but keeps the compatibility of output video and input audio, allowing for a more tailored and effective portrayal of distinct personas across different languages. The significant improvements in the fidelity of animated portraits, the accuracy of lip-syncing, and the appropriate motion variations achieved by our method render it a versatile tool for animating any portrait in any language.