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MMM: Generative Masked Motion Model
March 29, 2024, 4:43 a.m. | Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee, Chen Chen
cs.LG updates on arXiv.org arxiv.org
Abstract: Recent advances in text-to-motion generation using diffusion and autoregressive models have shown promising results. However, these models often suffer from a trade-off between real-time performance, high fidelity, and motion editability. To address this gap, we introduce MMM, a novel yet simple motion generation paradigm based on Masked Motion Model. MMM consists of two key components: (1) a motion tokenizer that transforms 3D human motion into a sequence of discrete tokens in latent space, and (2) …
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