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arXiv · 2605.30925

MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance

Nathan Sala·Ofir Abramovich·Ariel Shamir·Daniel Cohen-Or·Andreas Aristidou·Sigal Raab
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Abstract

Text-to-motion generation has progressed rapidly in recent years, offering an expressive interface for animation and human-computer interaction. However, current models remain brittle when handling prompts that describe multiple actions occurring at the same time. Rather than realizing all components of a composite description, models frequently prioritize a single dominant action and neglect the rest, leading to incomplete or ambiguous motion. We present MultiAct, an unpaired, inference-time framework for compositional text-to-motion synthesis that operates directly on pretrained motion generators without retraining or architectural modification. Our method counteracts semantic collapse by adaptively amplifying cross-attention scores associated with underrepresented prompt components. We note that effective modulation depends on prompt-specific choices, such as which tokens and layers to target, and introduce a lightweight auxiliary decision scheme that determines the most effective attention-strengthening parametrization. Extensive quantitative and qualitative evaluations demonstrate that MultiAct consistently outperforms existing baselines on composite prompts, achieving improved semantic coverage while preserving motion realism. Project page: https://natsala13.github.io/multiact.github.io.

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Nathan Sala, Ofir Abramovich, Ariel Shamir, Daniel Cohen-Or, Andreas Aristidou, Sigal Raab. 2026-05-29. MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance. https://arxiv.org/abs/2605.30925

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