From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech
This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations.To this end, we propose a multi-modal learning framework that links the inference stage and generation stage.First, the inference networks are trained to match the speaker identity between the two different modalities.Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice.
This paper proposes a method of end-to-end multi-modal generation of human face from speech based on a self-supervised learning framework.This paper presents a multi-modal learning framework that links the inference stage and generation stage for seeking the possibility of generating the human face from voice solely.This work aims to build one conditional face image generation framework from the audio signal. 