Absolute Coordinates Make Motion Generation Easy

• URL: http://arxiv.org/abs/2505.19377v2
• Date: Fri, 30 May 2025 19:54:43 GMT
• Title: Absolute Coordinates Make Motion Generation Easy
• Authors: Zichong Meng, Zeyu Han, Xiaogang Peng, Yiming Xie, Huaizu Jiang,
• Abstract summary: State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D.<n>We propose a radically simplified and long-abandoned alternative for text-to-motion generation: absolute joint coordinates in global space.
• Score: 8.153961351540834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D, which encodes motion relative to the pelvis and to the previous frame with built-in redundancy. While this design simplifies training for earlier generation models, it introduces critical limitations for diffusion models and hinders applicability to downstream tasks. In this work, we revisit the motion representation and propose a radically simplified and long-abandoned alternative for text-to-motion generation: absolute joint coordinates in global space. Through systematic analysis of design choices, we show that this formulation achieves significantly higher motion fidelity, improved text alignment, and strong scalability, even with a simple Transformer backbone and no auxiliary kinematic-aware losses. Moreover, our formulation naturally supports downstream tasks such as text-driven motion control and temporal/spatial editing without additional task-specific reengineering and costly classifier guidance generation from control signals. Finally, we demonstrate promising generalization to directly generate SMPL-H mesh vertices in motion from text, laying a strong foundation for future research and motion-related applications.

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