title: EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with
    Audio2Video Diffusion Model under Weak Conditions
summary: In this work, we tackle the challenge of enhancing the realism and
  expressiveness in talking head video generation by focusing on the dynamic and
  nuanced relationship between audio cues and facial movements. We identify the
  limitations of traditional techniques that often fail to capture the full
  spectrum of human expressions and the uniqueness of individual facial styles.
  To address these issues, we propose EMO, a novel framework that utilizes a
  direct audio-to-video synthesis approach, bypassing the need for intermediate
  3D models or facial landmarks. Our method ensures seamless frame transitions
  and consistent identity preservation throughout the video, resulting in highly
  expressive and lifelike animations. Experimental results demonsrate that EMO is
  able to produce not only convincing speaking videos but also singing videos in
  various styles, significantly outperforming existing state-of-the-art
  methodologies in terms of expressiveness and realism.
paper_authors._id: 65deacc617d90ff441877aaf
paper_authors.name: Linrui Tian
paper_authors._id: 65deacc617d90ff441877ab0
paper_authors.name: Qi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-28T12:09:33.691Z
paper_authors._id: 65df1f1ee98700500d4c289c
paper_authors.avatarUrl: /avatars/be11bf61465df29ac997cc0fedad1cb9.svg
paper_authors.fullname: qi wang
paper_authors.type: user
paper_authors.user: lucaskingjade
paper_authors._id: 65deacc617d90ff441877ab1
paper_authors.name: Bang Zhang
paper_authors._id: 65deacc617d90ff441877ab2
paper_authors.name: Liefeng Bo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-28T11:45:36.931Z
paper_authors._id: 63d0cc736b985b0f25d0412c
paper_authors.avatarUrl: /avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
paper_authors.fullname: Bo
paper_authors.type: user
paper_authors.user: Liefeng
paper_title: EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with
    Audio2Video Diffusion Model under Weak Conditions
paper_summary: In this work, we tackle the challenge of enhancing the realism and
  expressiveness in talking head video generation by focusing on the dynamic and
  nuanced relationship between audio cues and facial movements. We identify the
  limitations of traditional techniques that often fail to capture the full
  spectrum of human expressions and the uniqueness of individual facial styles.
  To address these issues, we propose EMO, a novel framework that utilizes a
  direct audio-to-video synthesis approach, bypassing the need for intermediate
  3D models or facial landmarks. Our method ensures seamless frame transitions
  and consistent identity preservation throughout the video, resulting in highly
  expressive and lifelike animations. Experimental results demonsrate that EMO is
  able to produce not only convincing speaking videos but also singing videos in
  various styles, significantly outperforming existing state-of-the-art
  methodologies in terms of expressiveness and realism.
paper_ai_summary: EMO, a direct audio-to-video synthesis framework, enhances realism and expressiveness in talking head video generation by capturing nuanced audio-facial relationships without intermediate models.
paper_ai_keywords: audio-to-video synthesis seamless frame transitions consistent identity preservation singing videos