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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 comb","title":"AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling","url":"https://arxiv.org/abs/2605.29488","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.29488v2 Announce Type: replace-cross \nAbstract: 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. 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