arxiv preprint - EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions

In this episode, we discuss EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions by Linrui Tian, Qi Wang, Bang Zhang, Liefeng Bo. The paper presents a new framework named EMO for generating realistic talking head videos, improving the synchronization between audio cues and facial movements. Traditional methods often miss the complexity of human expressions and individual facial characteristics, but EMO overcomes these limitations by directly converting audio to video without relying on 3D models or facial landmarks. This direct synthesis approach results in more expressive and seamlessly animated portrait videos that are better aligned with the audio.