Abstract
Video generation models use the first frame as a conceptual memory buffer, enabling robust customization with minimal training examples.
What role does the first frame play in video generation models? Traditionally, it's viewed as the spatial-temporal starting point of a video, merely a seed for subsequent animation. In this work, we reveal a fundamentally different perspective: video models implicitly treat the first frame as a conceptual memory buffer that stores visual entities for later reuse during generation. Leveraging this insight, we show that it's possible to achieve robust and generalized video content customization in diverse scenarios, using only 20-50 training examples without architectural changes or large-scale finetuning. This unveils a powerful, overlooked capability of video generation models for reference-based video customization.
Community
Github:
https://github.com/zli12321/FFGO-Video-Customization
Project Page:
http://firstframego.github.io
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