When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908949813395456 |
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| author | Sun, Zhengyang Chen, Yu Zhou, Xin Li, Xiaofan Chen, Xiwu Liang, Dingkang Bai, Xiang |
| author_facet | Sun, Zhengyang Chen, Yu Zhou, Xin Li, Xiaofan Chen, Xiwu Liang, Dingkang Bai, Xiang |
| contents | Text-to-video diffusion models have enabled open-ended video synthesis, but often struggle with generating the correct number of objects specified in a prompt. We introduce NUMINA , a training-free identify-then-guide framework for improved numerical alignment. NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout. It then refines this layout conservatively and modulates cross-attention to guide regeneration. On the introduced CountBench, NUMINA improves counting accuracy by up to 7.4% on Wan2.1-1.3B, and by 4.9% and 5.5% on 5B and 14B models, respectively. Furthermore, CLIP alignment is improved while maintaining temporal consistency. These results demonstrate that structural guidance complements seed search and prompt enhancement, offering a practical path toward count-accurate text-to-video diffusion. The code is available at https://github.com/H-EmbodVis/NUMINA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08546 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models Sun, Zhengyang Chen, Yu Zhou, Xin Li, Xiaofan Chen, Xiwu Liang, Dingkang Bai, Xiang Computer Vision and Pattern Recognition Text-to-video diffusion models have enabled open-ended video synthesis, but often struggle with generating the correct number of objects specified in a prompt. We introduce NUMINA , a training-free identify-then-guide framework for improved numerical alignment. NUMINA identifies prompt-layout inconsistencies by selecting discriminative self- and cross-attention heads to derive a countable latent layout. It then refines this layout conservatively and modulates cross-attention to guide regeneration. On the introduced CountBench, NUMINA improves counting accuracy by up to 7.4% on Wan2.1-1.3B, and by 4.9% and 5.5% on 5B and 14B models, respectively. Furthermore, CLIP alignment is improved while maintaining temporal consistency. These results demonstrate that structural guidance complements seed search and prompt enhancement, offering a practical path toward count-accurate text-to-video diffusion. The code is available at https://github.com/H-EmbodVis/NUMINA. |
| title | When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.08546 |