MoScale: Autoregressive Next-Scale Prediction for Human Motion Generation and Editing

We present ScaleMoGen, a scale-wise autoregressive frame-work for text-driven human motion generation. Unlike conventional au-toregressive approaches that rely on standard next-token prediction, Scale-MoGen frames motion generation as a coarse-to-fine process. We quantize3D motions into compositional discrete tokens across multiple skeletal-temporal scales of increasing granularity, learning to generate motionby autoregressively predicting next-scale token maps. To maintain struc-tural integrity, our motion tokenizers and quantizers are explicitly de-signed so that discrete tokens at every scale strictly preserve the skeletalhierarchy. Additionally, we employ bitwise quantization and prediction,which efficiently scale up the tokenizer vocabulary to preserve motiondetails and stabilize optimization. Extensive experiments demonstratethat ScaleMoGen achieves state-of-the-art performance, establishing anFID of 0.030 (vs. 0.045 for MoMask) on HumanML3D and a CLIP Scoreof 0.693 (vs. 0.685 for MoMask++) on the SnapMoGen dataset. Further-more, we demonstrate that our skeletal-temporal multi-scale representa-tion naturally facilitates training-free, text-guided motion editing.