Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
6 This section presents a matrix reformulation of Eq
2 Pith papers cite this work, alongside 365 external citations. Polarity classification is still indexing.
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GenSP learns a continuous neural deformation model from sphere coordinates and latent codes to produce consistent spherical parameterizations for genus-0 shapes.
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Unleashing Infinite Motion: Scaling Expressive Quadrupedal Motion via Generative Video Priors
Uni-Mo generates 7,488 language-annotated quadruped motions via LLM prompts and video diffusion, lifts them to 3D trajectories, and trains policies achieving 96.7% real-robot success on 392 sampled motions.
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GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models
GenSP learns a continuous neural deformation model from sphere coordinates and latent codes to produce consistent spherical parameterizations for genus-0 shapes.