Making Video Models Better Follow User Intent
Cohere_Labs · x · 2026-07-14
Cohere Labs' Computer Vision community is promoting a session on how video generation models can more faithfully adhere to user intent, focusing on modifying videos via minor edits rather than re-rendering entire clips.
The post highlights current issues with video diffusion pipelines:
- High costs
- Fragility with fine-grained edits
- The necessity to regenerate the whole video for minor changes
Daniel Ajisafe's work aims to solve this pain point. The related paper, titled "Making Video Models Adhere to User Intent with Minor Adjustments," will be presented at the CVPR 2026 AI for Creative Visual Content Workshop. The author also shares a counterintuitive finding: applying a slight offset to control signals might actually yield better control consistency.
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