HarmoniVox: Painting Voices to Match the Avatar's Soul

Imagine James Bond speaking like Mr. Bean---such a mismatch would create a jarring dissonance and break the viewer's immersion. Current research on virtual avatar animation has focused on modeling 3D geometry, appearance, motion generation, however, neglecting the harmony between speech prosody and the avatar's visual presentation and contextual environment. In this paper, we seek to bridge this gap by firstly identifying and defining the key elements necessary for achieving audiovisual harmony, such as appearance, expression, body posture, backgrounds and colors. Subsequently, we propose a method that jointly models semantic consistency in avatar animation, named HarmoniVox, specifically on crafting prosodic speech consistent with the avatar's essence from given visual image. To achieve this, we implement a technical framework with a mutual modal contrastive learning strategy, enhancing multimodal alignment in a coarse-to-fine fashion. To support this method, we establish a experimental dataset HarAvaSpeech comprising 28,929 image-audio pairs, designed to encompass expressive speech prosody and rich avatar visual presentations across a wide range of contexts. Leveraging this dataset, our experiments demonstrate that the proposed method outperforms the baselines in manipulating the nuanced tone and harmonious rhythm of speech with the avatar visual presentations, and reveal generalizability on out-of-domain cases. Demo would be provided in https://harmonivox.github.io/harmonivox/.