Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2026

Abstract

Long-term motion generation is a challenging task that requires producing coherent and realistic sequences over extended durations. Current methods primarily rely on framewise motion representations, which capture only static spatial details and overlook temporal dynamics. This approach leads to significant redundancy across the temporal dimension, complicating the generation of effective long-term motion. To overcome these limitations, we introduce the novel concept of Lagrangian Motion Fields, specifically designed for long-term motion generation. By treating each joint as a Lagrangian particle with uniform velocity over short intervals, our approach condenses motion representations into a series of "supermotions" (analogous to superpixels). This method seamlessly integrates static spatial information with interpretable temporal dynamics, transcending the limitations of existing network architectures and motion sequence content types. Our solution is versatile and lightweight, eliminating the need for neural network preprocessing. Our approach excels in tasks such as long-term music-to-dance generation and text-to-motion generation, offering enhanced efficiency, superior generation quality, and greater diversity compared to existing methods. Additionally, the adaptability of Lagrangian Motion Fields extends to applications like infinite motion looping and fine-grained controlled motion generation, highlighting its broad utility.

Keywords

Motion segmentation, Three-dimensional displays, Dynamics, Trajectory, Training, Image color analysis, Humanities, Redundancy, Pipelines, Diffusion models, Motion generation, animation, motion representations

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

48

Issue

2

First Page

1171

Last Page

1184

ISSN

0162-8828

Identifier

10.1109/TPAMI.2025.3612380

Publisher

IEEE

Embargo Period

3-19-2026

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TPAMI.2025.3612380

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