ScaleMoGen introduces a scale-wise autoregressive framework that quantizes motions into hierarchical discrete tokens and predicts next-scale maps to achieve SOTA FID 0.030 on HumanML3D and text-guided editing.
Rethinking diffusion for text-driven human motion generation
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Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.
MARRS synthesizes fine-grained reaction motions via unit-distinguished VAE, masked action-conditioned fusion, mutual unit modulation, and compact MLP diffusion predictors.
citing papers explorer
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ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation
ScaleMoGen introduces a scale-wise autoregressive framework that quantizes motions into hierarchical discrete tokens and predicts next-scale maps to achieve SOTA FID 0.030 on HumanML3D and text-guided editing.
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SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.
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MARRS: Masked Autoregressive Unit-based Reaction Synthesis
MARRS synthesizes fine-grained reaction motions via unit-distinguished VAE, masked action-conditioned fusion, mutual unit modulation, and compact MLP diffusion predictors.
- Next-Scale Autoregressive Models for Text-to-Motion Generation