Human Motion and Animation
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Abstract
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.
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@article{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},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2605.29488},
url = {https://doi.org/10.48550/arxiv.2605.29488}
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