Neural Motion Blending Across Arbitrary Character Topologies
A novel framework for motion blending across heterogeneous skeletons is presented, which combines a semantic encoder, which extracts per-frame latent representations of the motion state, with a diffusion-based decoder, which reconstructs character-specific motion conditioned on this latent code.