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AnyMo framework generates human motion from diverse inputs

By PulseAugur Editorial · [3 sources] ·

Researchers have introduced AnyMo, a novel framework for generating human motion conditioned on various modalities like text, speech, and music. This approach utilizes a masked modeling transformer and a motion tokenizer, trained on the newly created OmniHuMo dataset, which contains over 5,000 hours of motion data with multimodal annotations. AnyMo aims to overcome limitations of previous methods by enabling flexible control and high-fidelity synthesis across arbitrary combinations of input signals. AI

IMPACT Enables more flexible and high-fidelity human motion generation from diverse multimodal inputs.

RANK_REASON This is a research paper describing a new model and dataset for motion generation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

AnyMo framework generates human motion from diverse inputs

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This is a research paper describing a new model and dataset for motion generation.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Yiheng Li, Zhuo Li, Ruibing Hou, Yingjie Chen, Hong Chang, Hao Liu, Shiguang Shan ·

    AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

    arXiv:2605.29488v1 Announce Type: cross 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 architectu…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

    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 scal…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

    A unified multimodal framework for human motion generation that combines a Residual FSQ-based motion tokenizer with a scalable masked modeling transformer to enable high-quality synthesis across arbitrary modality combinations.

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