T2M Mamba: Motion Periodicity-Saliency Coupling Approach for Stable Text-Driven Motion Generation

• URL: http://arxiv.org/abs/2602.01352v1
• Date: Sun, 01 Feb 2026 17:42:53 GMT
• Title: T2M Mamba: Motion Periodicity-Saliency Coupling Approach for Stable Text-Driven Motion Generation
• Authors: Xingzu Zhan, Chen Xie, Honghang Chen, Yixun Lin, Xiaochun Mai,
• Abstract summary: Text-to-motion generation has attracted increasing attention in fields such as avatar animation and humanoid robotic interaction.<n>Models treat motion periodicity and saliency as independent factors, overlooking their coupling and causing generation drift in long sequences.<n>We propose T2M Mamba to address these limitations by (i) proposing Periodicity-Saliency Aware Mamba.
• Score: 3.6564162676635363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-motion generation, which converts motion language descriptions into coherent 3D human motion sequences, has attracted increasing attention in fields, such as avatar animation and humanoid robotic interaction. Though existing models have achieved significant fidelity, they still suffer from two core limitations: (i) They treat motion periodicity and keyframe saliency as independent factors, overlooking their coupling and causing generation drift in long sequences. (ii) They are fragile to semantically equivalent paraphrases, where minor synonym substitutions distort textual embeddings, propagating through the decoder and producing unstable or erroneous motions. In this work, we propose T2M Mamba to address these limitations by (i) proposing Periodicity-Saliency Aware Mamba, which utilizes novel algorithms for keyframe weight estimation via enhanced Density Peaks Clustering and motion periodicity estimation via FFT-accelerated autocorrelation to capture coupled dynamics with minimal computational overhead, and (ii) constructing a Periodic Differential Cross-modal Alignment Module (PDCAM) to enhance robust alignment of textual and motion embeddings. Extensive experiments on HumanML3D and KIT-ML datasets have been conducted, confirming the effectiveness of our approach, achieving an FID of 0.068 and consistent gains on all other metrics.

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