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📖 Abstract

Large-scale video generative models have recently demonstrated strong visual capabilities. In this work, we investigate whether such capabilities can be harnessed for controllable image-to-video generation by interpreting visual signals embedded within the frames as instructions, a paradigm we term In-Video Instruction.

In contrast to prompt-based control, which provides textual descriptions that are inherently global and coarse, In-Video Instruction encodes user guidance directly into the visual domain through elements such as overlaid text, arrows, or trajectories. This enables explicit, spatial-aware, and unambiguous correspondences between visual subjects and their intended actions by assigning distinct instructions to different objects.

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🎥 Generative Results

Example 01	Example 02
Example 03	Example 04
Example 05	Example 06
Example 07	Example 08
Example 09	Example 10 (Kling 2.5)
Example 11 (Kling 2.5)	Example 12 (Kling 2.5)

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📝 BibTeX

@article{fang2025invideo,
  title={In-Video Instructions: Visual Signals as Generative Control},
  author={Fang, Gongfan and Ma, Xinyin and Wang, Xinchao},
  journal={arXiv preprint arXiv:2511.19401},
  year={2025}
}