MotionMaestro: Masked Tokenization for Unified Motion Generation

arXiv:2609.37495 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Project page: https://kaist-viclab.github.io/MotionMaestro_site

Terminology

• Tokenization — 10× in this paper · explained in 2 episodes
• Flow Matching — 9× in this paper · explained in 7 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Latent Space — 7× in this paper · explained in 7 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Fidelity — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Frechet Inception Distance — 1× in this paper
• Generative Modeling — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Joint Attention — 1× in this paper · explained in 2 episodes
• Korea Advanced Institute of Science and Technology — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes

Sources

• UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
• Flow Matching for Generative Modeling
• Human Motion Diffusion Model
• HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation

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