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Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models

arXiv – CS AI|Sangwon Jang, Taekyung Ki, Jaehyeong Jo, Jaehong Yoon, Soo Ye Kim, Zhe Lin, Sung Ju Hwang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce Frame Guidance, a training-free method for controllable video generation using diffusion models. The technique enables fine-grained control over video generation through frame-level signals like keyframes and style references without requiring expensive fine-tuning of large-scale models.

Key Takeaways

→Frame Guidance offers training-free controllable video generation using frame-level signals such as keyframes, style references, sketches, or depth maps.
→The method includes a latent processing technique that dramatically reduces memory usage compared to traditional approaches.
→Frame Guidance is compatible with any video diffusion models without requiring fine-tuning or retraining.
→The technique enables diverse video generation tasks including keyframe guidance, stylization, and video looping.
→Experimental results demonstrate high-quality controlled video output across a wide range of input signals and tasks.

#video-generation #diffusion-models #training-free #controllable-ai #computer-vision #frame-guidance #video-diffusion #ai-research

Read Original →via arXiv – CS AI

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