: Available on arXiv at https://arxiv.org/pdf/2208.15001 .
Published in , this paper introduced the first diffusion-based framework for generating diverse and controllable human motions from natural language descriptions. 220815001323 rar
[2208.15001] MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model. > cs > arXiv:2208.15001. : Available on arXiv at https://arxiv
: It excels at modeling complicated data distributions, producing more vivid and varied movements than previous methods. 220815001323 rar
: Allows for body-part-level control and motion interpolation.
: Users can specify complex instructions (e.g., "a person walking while waving").
: 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