A latent diffusion model over continuous implicit neural representations samples INR parameters from sparse keyframes to reconstruct plausible, smooth, and diverse motions while preserving keyframe accuracy.
Deepphase: periodic autoencoders for learning motion phase manifolds,
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HALO learns latent reduced-order models with Poincaré maps for hybrid locomotion dynamics, allowing Lyapunov-based regions of attraction to be lifted from latent space to the full-order system.
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Generative Motion In-betweening by Diffusion over Continuous Implicit Representations
A latent diffusion model over continuous implicit neural representations samples INR parameters from sparse keyframes to reconstruct plausible, smooth, and diverse motions while preserving keyframe accuracy.
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HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincar\'e Maps, and Regions of Attraction
HALO learns latent reduced-order models with Poincaré maps for hybrid locomotion dynamics, allowing Lyapunov-based regions of attraction to be lifted from latent space to the full-order system.