Möbius moves denoising inside the language model: no motion VAE, and it leads HumanML3D on generation and captioning
Möbius places the entire denoising process of motion generation inside the same multimodal representation space used for motion understanding, so clean motion, noisy motion and text share attention without a discrete tokenizer or a pretrained motion VAE. On HumanML3D, the 0.6B variant outperforms diffusion-based, language-model-based and unified motion-language baselines on both text-to-motion generation and motion captioning; scaling to 1.7B parameters and 1,700 hours of motion data improves all motion-language tasks further.