T3M: Text Guided 3D Human Motion Synthesis from Speech
Authors: name: Wenshuo Peng, name: Kaipeng Zhang, name: Sai Qian Zhang

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}