NeurIPS 2025

Deep Compositional Phase Diffusion for Long Motion
Sequence Generation
Ho Yin Au
Hong Kong Baptist University
cshyau@comp.hkbu.edu.hk
Jie Chen∗
Hong Kong Baptist University
chenjie@comp.hkbu.edu.hk
Junkun Jiang
Hong Kong Baptist University
csjkjiang@comp.hkbu.edu.hk
Jingyu Xiang
Hong Kong Baptist University
csjyxiang@comp.hkbu.edu.hk
(c) Long-term Motion Generation
[Walk forward (2s)]
[Turn right (2s)]
[Walk with hands 
above head (4s)]
[Lean down (3s)]
[Leap forward (3s)]
[Leap forward (2s)]
(a) Compositional Motion Generation
(b) Compositional Motion Inbetweening
[Turn right (3s)]
User input motion
User input motion
[Walk forward (4s)]
[Walk clockwise (4s)]
[Jump forward (4s)]
[Walk forward (3s)]
[Crawling forward (5s)]
Figure 1: Our Compositional Phase Diffusion framework produces high-quality composite motion
sequences with smooth transitions and semantic alignment. (a) Compositional generation involves
synthesizing multiple motion segments of varying lengths simultaneously, ensuring smooth transitions
between segments. (b) Motion inbetweening allows users to select segments (in blue and yellow) and
create conditional or unconditional bridging motions. (c) Long-term motion generation is achieved
by scaling the framework with additional modules, enabling the parallel denoising of a larger number
of motion segments. The rainbow color indicates time progression.
Abstract
Recent research on motion generation has shown significant progress in generating
semantically aligned motion with singular semantics. However, when employ-
ing these models to create composite sequences containing multiple semantically
generated motion clips, they often struggle to preserve the continuity of motion
dynamics at the transition boundaries between clips, resulting in awkward transi-
tions and abrupt artifacts. To address these challenges, we present Compositional
Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM)
and Transitional Phase Diffusion Module (TPDM) ...