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Published in , this paper introduced the first diffusion-based framework for generating diverse and controllable human motions from natural language descriptions.
: Available on arXiv at https://arxiv.org/pdf/2208.15001 . 220815001323 rar
: It excels at modeling complicated data distributions, producing more vivid and varied movements than previous methods. Published in , this paper introduced the first
: 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 220815001323 rar
: Includes demos and code at mingyuan-zhang.github.io/projects/MotionDiffuse.html . Text-Driven Human Motion Generation with Diffusion Model
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