WANDR: Intention-guided Human Motion Generation

arXiv:2404.15383v1 Announce Type: new
Abstract: Synthesizing natural human motions that enable a 3D human avatar to walk and reach for arbitrary goals in 3D space remains an unsolved problem with many applications. Existing methods (data-driven or using reinforcement learning) are limited in terms of generalization and motion naturalness. A primary obstacle is the scarcity of training data that combines locomotion with goal reaching. To address this, we introduce WANDR, a data-driven model that takes an avatar's initial pose and a …

abstract applications arxiv avatar cs.ai cs.cv data data-driven human natural reinforcement reinforcement learning space terms type unsolved