Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2604.17090v1
• Date: Sat, 18 Apr 2026 17:58:50 GMT
• Title: Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition
• Abstract summary: This work investigates unified action recognition and motion generation by leveraging skeleton coordinates for both motion understanding and generation.<n>We propose Coordinates-based Autoregressive Motion Diffusion (CoAMD), which synthesizes motion in a coarse-to-fine manner.<n>Our model can be applied to four important tasks, including skeleton-based action recognition, text-to-motion generation, text-motion retrieval, and motion editing.
• Score: 26.447920160010515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human action recognition and motion generation are two active research problems in human-centric computer vision, both aiming to align motion with textual semantics. However, most existing works study these two problems separately, without uncovering the links between them, namely that motion generation requires semantic comprehension. This work investigates unified action recognition and motion generation by leveraging skeleton coordinates for both motion understanding and generation. We propose Coordinates-based Autoregressive Motion Diffusion (CoAMD), which synthesizes motion in a coarse-to-fine manner. As a core component of CoAMD, we design a Multi-modal Action Recognizer (MAR) that provides gradient-based semantic guidance for motion generation. Furthermore, we establish a rigorous benchmark by evaluating baselines on absolute coordinates. Our model can be applied to four important tasks, including skeleton-based action recognition, text-to-motion generation, text-motion retrieval, and motion editing. Extensive experiments on 13 benchmarks across these tasks demonstrate that our approach achieves state-of-the-art performance, highlighting its effectiveness and versatility for human motion modeling. Code is available at https://github.com/jidongkuang/CoAMD.

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