title: T3M: Text Guided 3D Human Motion Synthesis from Speech
summary: Speech-driven 3D motion synthesis seeks to create lifelike animations based
  on human speech, with potential uses in virtual reality, gaming, and the film
  production. Existing approaches reply solely on speech audio for motion
  generation, leading to inaccurate and inflexible synthesis results. To mitigate
  this problem, we introduce a novel text-guided 3D human motion synthesis
  method, termed T3M. Unlike traditional approaches, T3M allows precise
  control over motion synthesis via textual input, enhancing the degree of
  diversity and user customization. The experiment results demonstrate that T3M
  can greatly outperform the state-of-the-art methods in both quantitative
  metrics and qualitative evaluations. We have publicly released our code at
  https://github.com/Gloria2tt/T3M.git{https://github.com/Gloria2tt/T3M.git}
paper_authors._id: 66cbf66257c2405fdffb2896
paper_authors.name: Wenshuo Peng
paper_authors._id: 66cbf66257c2405fdffb2897
paper_authors.name: Kaipeng Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-17T11:28:45.070Z
paper_authors._id: 65f1713552c38a91e0a445e8
paper_authors.avatarUrl: /avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
paper_authors.fullname: kaipeng
paper_authors.type: user
paper_authors.user: kpzhang996
paper_authors._id: 66cbf66257c2405fdffb2898
paper_authors.name: Sai Qian Zhang
paper_title: T3M: Text Guided 3D Human Motion Synthesis from Speech
paper_summary: Speech-driven 3D motion synthesis seeks to create lifelike animations based
  on human speech, with potential uses in virtual reality, gaming, and the film
  production. Existing approaches reply solely on speech audio for motion
  generation, leading to inaccurate and inflexible synthesis results. To mitigate
  this problem, we introduce a novel text-guided 3D human motion synthesis
  method, termed T3M. Unlike traditional approaches, T3M allows precise
  control over motion synthesis via textual input, enhancing the degree of
  diversity and user customization. The experiment results demonstrate that T3M
  can greatly outperform the state-of-the-art methods in both quantitative
  metrics and qualitative evaluations. We have publicly released our code at
  https://github.com/Gloria2tt/T3M.git{https://github.com/Gloria2tt/T3M.git}
paper_ai_keywords: speech-driven 3D motion synthesis text-guided 3D human action synthesis T3M motion association textual input end-to-end quantitative metrics qualitative evaluations