Temporal Specification Guidance for Text-to-Video Generation
Quick Answer
This paper shows that Temporal Specification Guidance (TSG) enhances text-to-video generation by converting temporal requirements into executable objectives, improving specification satisfaction accuracy by 9.5-20 percentage points across five models without the need for external classifiers or backpropagation.
Quick Take
This framework utilizes differentiable quantitative semantics and attention biases to guide video generation effectively.
Key Points
- TSG scores visual concept traces using differentiable quantitative semantics.
- Improves specification satisfaction accuracy by 9.5-20 percentage points.
- No external concept classifier or backpropagation required.
- Framework tested on 220 prompts across 11 temporal constraint categories.
- Attention-steered forward pass enhances classifier-free guidance (CFG).
Paper Resources
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~2 min readAbstract:Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and persistence requirements. We propose representing these requirements as specifications in a formal Temporal Logic, and introduce Temporal Specification Guidance (TSG), a training-free inference-time framework that turns temporal specifications into executable objectives for guiding video generation during sampling. TSG scores traces of visual concept evidence using differentiable quantitative semantics and uses the score's gradients to construct a specification-guided denoising direction. We instantiate this framework with video-text attention traces, efficient differentiable scores for Metric Temporal Logic, and gradient-derived attention biases. This instantiation requires neither an external concept classifier nor backpropagation through the video transformer. Its attention-steered forward pass yields a denoising direction that augments classifier-free guidance (CFG). On a benchmark of 220 prompts spanning 11 categories of temporal constraints, including ordered object entry and state transitions, this instantiation improves specification satisfaction accuracy over CFG by 9.5-20 percentage points across five models, with minimal memory overhead. These results demonstrate the potential of temporal specifications and differentiable satisfaction objectives as a flexible interface for controlling video generation.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.20822 [cs.AI] |
| (or arXiv:2610.20822v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20822 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Avishree Khare [view email]
[v1]
Wed, 14 Oct 2026 19:09:33 UTC (23,419 KB)
— Originally published at arxiv.org
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