title: EMO2: End-Effector Guided Audio-Driven Avatar Video Generation
summary: In this paper, we propose a novel audio-driven talking head method capable of
  simultaneously generating highly expressive facial expressions and hand
  gestures. Unlike existing methods that focus on generating full-body or
  half-body poses, we investigate the challenges of co-speech gesture generation
  and identify the weak correspondence between audio features and full-body
  gestures as a key limitation. To address this, we redefine the task as a
  two-stage process. In the first stage, we generate hand poses directly from
  audio input, leveraging the strong correlation between audio signals and hand
  movements. In the second stage, we employ a diffusion model to synthesize video
  frames, incorporating the hand poses generated in the first stage to produce
  realistic facial expressions and body movements. Our experimental results
  demonstrate that the proposed method outperforms state-of-the-art approaches,
  such as CyberHost and Vlogger, in terms of both visual quality and
  synchronization accuracy. This work provides a new perspective on audio-driven
  gesture generation and a robust framework for creating expressive and natural
  talking head animations.
paper_authors._id: 6790856e3b0a6384a4117d0e
paper_authors.name: Linrui Tian
paper_authors._id: 6790856e3b0a6384a4117d0f
paper_authors.name: Siqi Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-22T16:01:58.376Z
paper_authors._id: 66f9af43e45f81e73c0327ea
paper_authors.avatarUrl: /avatars/30976181ab540fb42a3377a2734a5793.svg
paper_authors.fullname: Siqi Hu
paper_authors.type: user
paper_authors.user: Siqi-Hu
paper_authors._id: 6790856e3b0a6384a4117d10
paper_authors.name: Qi Wang
paper_authors._id: 6790856e3b0a6384a4117d11
paper_authors.name: Bang Zhang
paper_authors._id: 6790856e3b0a6384a4117d12
paper_authors.name: Liefeng Bo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-22T16:02:08.503Z
paper_authors._id: 63d0cc736b985b0f25d0412c
paper_authors.avatarUrl: /avatars/3eb8c79f9a7c4c819038ea7b04e323dd.svg
paper_authors.fullname: Bo
paper_authors.type: user
paper_authors.user: Liefeng
paper_title: EMO2: End-Effector Guided Audio-Driven Avatar Video Generation
paper_summary: In this paper, we propose a novel audio-driven talking head method capable of
  simultaneously generating highly expressive facial expressions and hand
  gestures. Unlike existing methods that focus on generating full-body or
  half-body poses, we investigate the challenges of co-speech gesture generation
  and identify the weak correspondence between audio features and full-body
  gestures as a key limitation. To address this, we redefine the task as a
  two-stage process. In the first stage, we generate hand poses directly from
  audio input, leveraging the strong correlation between audio signals and hand
  movements. In the second stage, we employ a diffusion model to synthesize video
  frames, incorporating the hand poses generated in the first stage to produce
  realistic facial expressions and body movements. Our experimental results
  demonstrate that the proposed method outperforms state-of-the-art approaches,
  such as CyberHost and Vlogger, in terms of both visual quality and
  synchronization accuracy. This work provides a new perspective on audio-driven
  gesture generation and a robust framework for creating expressive and natural
  talking head animations.
paper_ai_summary: A novel audio-driven talking head method generates expressive facial expressions and hand gestures using a two-stage process, combining direct audio-to-hand-pose generation and a diffusion model for video synthesis.
paper_ai_keywords: audio-driven talking head facial expressions hand gestures co-speech gesture generation diffusion model video frames CyberHost Vlogger synchronization accuracy