In-Video Instructions: Visual Signals as Generative Control
Large-scale video generative models have recently demonstrated strong visual capabilities, enabling the prediction of future frames that adhere to the logical and physical cues in the current observation. 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. Extensive experiments on three state-of-the-art generators, including Veo 3.1, Kling 2.5, and Wan 2.2, show that video models can reliably interpret and execute such visually embedded instructions, particularly in complex multi-object scenarios.
692523c616eb3a9f1310395f
Gongfan Fang
692523c616eb3a9f13103960
Xinyin Ma
admin_assigned
2025-11-25T09:38:13.400Z
64396ebc21221ac7411852b3
https://cdn-avatars.huggingface.co/v1/production/uploads/64396ebc21221ac7411852b3/SR0dC8N0bdj9tZFxYPpSf.jpeg
Xinyin Ma
horseee
user
horseee
692523c616eb3a9f13103961
Xinchao Wang
In-Video Instructions: Visual Signals as Generative Control
Large-scale video generative models have recently demonstrated strong visual capabilities, enabling the prediction of future frames that adhere to the logical and physical cues in the current observation. 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. Extensive experiments on three state-of-the-art generators, including Veo 3.1, Kling 2.5, and Wan 2.2, show that video models can reliably interpret and execute such visually embedded instructions, particularly in complex multi-object scenarios.
Video generative models can interpret and execute visual instructions embedded within frames, enhancing controllability in image-to-video generation.
video generative models In-Video Instruction prompt-based control Veo 3.1 Kling 2.5 Wan 2.2 visual instructions overlaid text arrows trajectories spatial-aware multi-object scenarios