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arXiv · 2503.14919

GenM$^3$: Generative Pretrained Multi-path Motion Model for Text Conditional Human Motion Generation

Junyu Shi·Lijiang Liu·Yong Sun·Zhiyuan Zhang·Jinni Zhou·Qiang Nie
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Abstract

Scaling up motion datasets is crucial to enhance motion generation capabilities. However, training on large-scale multi-source datasets introduces data heterogeneity challenges due to variations in motion content. To address this, we propose Generative Pretrained Multi-path Motion Model (GenM\(^3\)), a comprehensive framework designed to learn unified motion representations. GenM\(^3\) comprises two components: 1) a Multi-Expert VQ-VAE (MEVQ-VAE) that adapts to different dataset distributions to learn a unified discrete motion representation, and 2) a Multi-path Motion Transformer (MMT) that improves intra-modal representations by using separate modality-specific pathways, each with densely activated experts to accommodate variations within that modality, and improves inter-modal alignment by the text-motion shared pathway. To enable large-scale training, we integrate and unify 11 high-quality motion datasets (approximately 220 hours of motion data) and augment it with textual annotations (nearly 10,000 motion sequences labeled by a large language model and 300+ by human experts). After training on our integrated dataset, GenM\(^3\) achieves a state-of-the-art FID of 0.035 on the HumanML3D benchmark, surpassing state-of-the-art methods by a large margin. It also demonstrates strong zero-shot generalization on IDEA400 dataset, highlighting its effectiveness and adaptability across diverse motion scenarios.

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Junyu Shi, Lijiang Liu, Yong Sun, Zhiyuan Zhang, Jinni Zhou, Qiang Nie. 2025-03-19. GenM$^3$: Generative Pretrained Multi-path Motion Model for Text Conditional Human Motion Generation. https://arxiv.org/abs/2503.14919

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