: It established a new state-of-the-art for the Text-to-Motion (T2M) task, influencing many subsequent models like MLD and StableMoFusion. Accessing the Paper
: Includes demos and code at mingyuan-zhang.github.io/projects/MotionDiffuse.html . Text-Driven Human Motion Generation with Diffusion Model 220815001323 rar
[2208.15001] MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model. > cs > arXiv:2208.15001. : It established a new state-of-the-art for the
: It excels at modeling complicated data distributions, producing more vivid and varied movements than previous methods. 220815001323 rar
The string likely refers to the arXiv identifier (specifically arXiv:2208.15001 ) for the academic paper titled "MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model" . Paper Overview: MotionDiffuse
: Allows for body-part-level control and motion interpolation.