When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models
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 whi...
| Authors | Zhengyang Sun, Yu Chen, Xin Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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