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

• URL: http://arxiv.org/abs/2503.14919v1
• Date: Wed, 19 Mar 2025 05:56:52 GMT
• Title: GenM$^3$: Generative Pretrained Multi-path Motion Model for Text Conditional Human Motion Generation
• Authors: Junyu Shi, Lijiang Liu, Yong Sun, Zhiyuan Zhang, Jinni Zhou, Qiang Nie,
• Abstract summary: Generative Pretrained Multi-path Motion Model (GenM$3$) is a framework designed to learn unified motion representations.<n>To enable large-scale training, we integrate and unify 11 high-quality motion datasets.<n>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.
• Score: 19.2804620329011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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