MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916064585056256 |
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| author | Sala, Nathan Abramovich, Ofir Shamir, Ariel Cohen-Or, Daniel Aristidou, Andreas Raab, Sigal |
| author_facet | Sala, Nathan Abramovich, Ofir Shamir, Ariel Cohen-Or, Daniel Aristidou, Andreas Raab, Sigal |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_30925 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance Sala, Nathan Abramovich, Ofir Shamir, Ariel Cohen-Or, Daniel Aristidou, Andreas Raab, Sigal Computer Vision and Pattern Recognition Graphics 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. |
| title | MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2605.30925 |