| title | AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling |
|---|---|
| authors | Yiheng Li, Zhuo Li, Ruibing Hou, Yingjie Chen, Hong Chang, Hao Liu, Shiguang Shan |
| date | 2026-05-28 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.29488 |
| affiliation | HumanMotionCapture |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Conditional human motion generation remains a fundamental challenge in computer vision and robotics. Despite significant progress, current methods are often constrained by fixed modality configurations and task-specific architectures, leaving cross-modal interactions and the scaling laws of multimodal-conditioned synthesis largely underexplored. A key bottleneck is the scarcity of large-scale modality-aligned motion data, limiting generalization across diverse control signals. In this work, we introduce OmniHuMo, a large-scale, high-quality dataset comprising over 5,000 hours of motion and 3.2 million sequences with precisely aligned multimodal annotations (e.g., text, speech, music, and trajectory). Leveraging OmniHuMo, we propose AnyMo, a unified multimodal framework combining a Residual FSQ-based motion tokenizer with a scalable masked modeling transformer, enabling high-quality motion synthesis under arbitrary modality combinations. Extensive experiments show that AnyMo achieves high-fidelity synthesis while offering flexible control over both spatial and stylistic attributes. |
cite
@misc{li2026anymo,
title={AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling},
author={Yiheng Li and Zhuo Li and Ruibing Hou and Yingjie Chen and Hong Chang and Hao Liu and Shiguang Shan},
year={2026},
eprint={2605.29488},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.29488}
}