Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging
This work addresses the challenge of synthesizing realistic facial images from vocal characteristics, with potential applications in public security and entertainment, though it appears incremental in improving existing methods.
The paper tackles the problem of generating facial images from speech by addressing limited image quality and poor facial similarity in existing methods, achieving a doubling of identity recall and an increase in quality score from 15 to 19.
While deep learning technologies are now capable of generating realistic images confusing humans, the research efforts are turning to the synthesis of images for more concrete and application-specific purposes. Facial image generation based on vocal characteristics from speech is one of such important yet challenging tasks. It is the key enabler to influential use cases of image generation, especially for business in public security and entertainment. Existing solutions to the problem of speech2face renders limited image quality and fails to preserve facial similarity due to the lack of quality dataset for training and appropriate integration of vocal features. In this paper, we investigate these key technical challenges and propose Speech Fusion to Face, or SF2F in short, attempting to address the issue of facial image quality and the poor connection between vocal feature domain and modern image generation models. By adopting new strategies on data model and training, we demonstrate dramatic performance boost over state-of-the-art solution, by doubling the recall of individual identity, and lifting the quality score from 15 to 19 based on the mutual information score with VGGFace classifier.