Bidirectional Autoregressive Motion Model (BAMM): A Novel Framework for Generating High-Quality and Editable Text-Driven Human Motions

BAMM is a novel text-to-motion generation framework that unifies generative masked modeling and autoregressive modeling to capture rich and bidirectional dependencies among motion tokens, while learning a direct probabilistic mapping from textual inputs to motion outputs with dynamically-adjusted motion sequence length.