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Found 2 papers, 1 papers with code
Flexible Motion In-betweening with Diffusion Models
To this end, we propose Conditional Motion Diffusion In-betweening (CondMDI) which allows for arbitrary dense-or-sparse keyframe placement and partial keyframe constraints while generating high-quality motions that are diverse and coherent with the given keyframes.
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Empirically, we find that the region density increases only moderately throughout training, as measured along fixed trajectories coming from the final policy.
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